Research article Special Issues

SAMS-Net: Fusion of attention mechanism and multi-scale features network for tumor infiltrating lymphocytes segmentation


  • Received: 30 July 2022 Revised: 16 November 2022 Accepted: 21 November 2022 Published: 01 December 2022
  • Automatic segmentation of tumor-infiltrating lymphocytes (TILs) from pathological images is essential for the prognosis and treatment of cancer. Deep learning technology has achieved great success in the segmentation task. It is still a challenge to realize accurate segmentation of TILs due to the phenomenon of blurred edges and adhesion of cells. To alleviate these problems, a squeeze-and-attention and multi-scale feature fusion network (SAMS-Net) based on codec structure, namely SAMS-Net, is proposed for the segmentation of TILs. Specifically, SAMS-Net utilizes the squeeze-and-attention module with the residual structure to fuse local and global context features and boost the spatial relevance of TILs images. Besides, a multi-scale feature fusion module is designed to capture TILs with large size differences by combining context information. The residual structure module integrates feature maps from different resolutions to strengthen the spatial resolution and offset the loss of spatial details. SAMS-Net is evaluated on the public TILs dataset and achieved dice similarity coefficient (DSC) of 87.2% and Intersection of Union (IoU) of 77.5%, which improved by 2.5% and 3.8% compared with UNet. These results demonstrate the great potential of SAMS-Net in TILs analysis and can further provide important evidence for the prognosis and treatment of cancer.

    Citation: Xiaoli Zhang, Kunmeng Liu, Kuixing Zhang, Xiang Li, Zhaocai Sun, Benzheng Wei. SAMS-Net: Fusion of attention mechanism and multi-scale features network for tumor infiltrating lymphocytes segmentation[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2964-2979. doi: 10.3934/mbe.2023140

    Related Papers:

    [1] Tariq Mahmood, Liaqat Ali, Muhammad Aslam, Ghulam Farid . On commutativity of quotient semirings through generalized derivations. AIMS Mathematics, 2023, 8(11): 25729-25739. doi: 10.3934/math.20231312
    [2] Liaqat Ali, Yaqoub Ahmed Khan, A. A. Mousa, S. Abdel-Khalek, Ghulam Farid . Some differential identities of MA-semirings with involution. AIMS Mathematics, 2021, 6(3): 2304-2314. doi: 10.3934/math.2021139
    [3] Saba Al-Kaseasbeh, Madeline Al Tahan, Bijan Davvaz, Mariam Hariri . Single valued neutrosophic (m,n)-ideals of ordered semirings. AIMS Mathematics, 2022, 7(1): 1211-1223. doi: 10.3934/math.2022071
    [4] Pakorn Palakawong na Ayutthaya, Bundit Pibaljommee . On n-ary ring congruences of n-ary semirings. AIMS Mathematics, 2022, 7(10): 18553-18564. doi: 10.3934/math.20221019
    [5] Abdelghani Taouti, Waheed Ahmad Khan . Fuzzy subnear-semirings and fuzzy soft subnear-semirings. AIMS Mathematics, 2021, 6(3): 2268-2286. doi: 10.3934/math.2021137
    [6] Rukhshanda Anjum, Saad Ullah, Yu-Ming Chu, Mohammad Munir, Nasreen Kausar, Seifedine Kadry . Characterizations of ordered h-regular semirings by ordered h-ideals. AIMS Mathematics, 2020, 5(6): 5768-5790. doi: 10.3934/math.2020370
    [7] Gurninder S. Sandhu, Deepak Kumar . A note on derivations and Jordan ideals of prime rings. AIMS Mathematics, 2017, 2(4): 580-585. doi: 10.3934/Math.2017.4.580
    [8] Gurninder S. Sandhu, Deepak Kumar . Correction: A note on derivations and Jordan ideals in prime rings. AIMS Mathematics, 2019, 4(3): 684-685. doi: 10.3934/math.2019.3.684
    [9] Faiza Shujat, Faarie Alharbi, Abu Zaid Ansari . Weak (p,q)-Jordan centralizer and derivation on rings and algebras. AIMS Mathematics, 2025, 10(4): 8322-8330. doi: 10.3934/math.2025383
    [10] Kaiqing Huang, Yizhi Chen, Miaomiao Ren . Additively orthodox semirings with special transversals. AIMS Mathematics, 2022, 7(3): 4153-4167. doi: 10.3934/math.2022230
  • Automatic segmentation of tumor-infiltrating lymphocytes (TILs) from pathological images is essential for the prognosis and treatment of cancer. Deep learning technology has achieved great success in the segmentation task. It is still a challenge to realize accurate segmentation of TILs due to the phenomenon of blurred edges and adhesion of cells. To alleviate these problems, a squeeze-and-attention and multi-scale feature fusion network (SAMS-Net) based on codec structure, namely SAMS-Net, is proposed for the segmentation of TILs. Specifically, SAMS-Net utilizes the squeeze-and-attention module with the residual structure to fuse local and global context features and boost the spatial relevance of TILs images. Besides, a multi-scale feature fusion module is designed to capture TILs with large size differences by combining context information. The residual structure module integrates feature maps from different resolutions to strengthen the spatial resolution and offset the loss of spatial details. SAMS-Net is evaluated on the public TILs dataset and achieved dice similarity coefficient (DSC) of 87.2% and Intersection of Union (IoU) of 77.5%, which improved by 2.5% and 3.8% compared with UNet. These results demonstrate the great potential of SAMS-Net in TILs analysis and can further provide important evidence for the prognosis and treatment of cancer.



    Semirings have significant applications in theory of automata, optimization theory, and in theoretical computer sciences (see [1,2,3]). A group of Russian mathematicians was able to create novel probability theory based on additive inverse semirings, called idempotent analysis (see[4,5]) having interesting applications in quantum physics. Javed et al. [6] identified a proper subclass of semirings known as MA-Semirings. The development of commutator identities and Lie type theory of semirings [6,7,8,9,10] and derivations [6,7,8,11,12] make this class quite interesting for researchers. To investigate commuting conditions for rings through certain differential identities and certain ideals are still interesting problems for researchers in ring theory (see for example [13,14,15,16,17,18,19]) and some of them are generalized in semirings (see [6,8,9,10,11,20]). In this paper we investigate commuting conditions of prime MA-semirings through certain differential identities and Jordan ideals (Theorems 2.5–2.8) and also study differential identities with the help of Jordan ideals (Theorem 2.3, Theorem 2.4, Theorem 2.10). In this connection we are able to generalize a few results of Oukhtite [21] in the setting of semirings. Now we present some necessary definitions and preliminaries which will be very useful for the sequel. By a semiring S, we mean a semiring with absorbing zero '0' in which addition is commutative. A semiring S is said to be additive inverse semiring if for each sS there is a unique sS such that s+s+s=s and s+s+s=s, where s denotes the pseudo inverse of s. An additive inverse semiring S is said to be an MA-semiring if it satisfies s+sZ(S),sS, where Z(S) is the center of S. The class of MA-semirings properly contains the class of distributive lattices and the class of rings, we refer [6,8,11,22] for examples. Throughout the paper by semiring S we mean an MA-semiring unless stated otherwise. A semiring S is prime if aSb={0} implies that a=0 or b=0 and semiprime if aSa={0} implies that a=0. S is 2-torsion free if for sS,2s=0 implies s=0. An additive mapping d:SS is a derivation if d(st)=d(s)t+sd(t). The commutator is defined as [s,t]=st+ts. By Jordan product, we mean st=st+ts for all s,tS. The notion of Jordan ideals was introduced by Herstein [23] in rings which is further extended canonically by Sara [20] for semirings. An additive subsemigroup G of S is called the Jordan ideal if sjG for all sS,jG. A mapping f:SS is commuting if [f(s),s]=0, sS. A mapping f:SS is centralizing if [[f(s),s],r]=0, s,rS. Next we include some well established identities of MA-semirings which will be very useful in the sequel. If s,t,zS and d is a derivation of S, then [s,st]=s[s,t], [st,z]=s[t,z]+[s,z]t, [s,tz]=[s,t]z+t[s,z], [s,t]+[t,s]=t(s+s)=s(t+t), (st)=st=st, [s,t]=[s,t]=[s,t], s(t+z)=st+sz, d(s)=(d(s)). To see more, we refer [6,7].

    From the literature we recall a few results of MA-semirings required to establish the main results.

    Lemma 1. [11] Let G be a Jordan ideal of an MA-semiring S. Then for all jG (a). 2[S,S]GG (b). 2G[S,S]G (c). 4j2SG (d). 4Sj2G (e). 4jSjG.

    Lemma 2. [11] Let S be a 2-torsion free prime MA-semiring and G a Jordan ideal of S. If aGb={0} then a=0 or b=0.

    In view of Lemma 1 and Lemma 2, we give some very useful remarks.

    Remark 1. [11]

    a). If r,s,tS,uG, then 2[r,st]uG.

    b). If aG={0} or Ga={0}, then a=0.

    Lemma 3. [12] Let G be a nonzero Jordan ideal and d be a derivation of a 2-torsion free prime MA-semiring S such that for all uG, d(u2)=0. Then d=0.

    Lemma 4. Let G be a nonzero Jordan ideal of a 2-torsion free prime MA-semiring S. If aS such that for all gG, [a,g2]=0. Then [a,s]=0,sS and hence aZ(S).

    Proof. Define a function da:SS by da(s)=[a,s], which is an inner derivation. As every inner derivation is derivation, therefore in view of hypothesis da is derivation satisfying da(g2)=[a,g2]=0,gG. By Lemma 3, da=0, which implies that da(s)=[a,s]=0, for all sS. Hence aZ(S).

    Lemma 5. Let S be a 2-torsion free prime MA-semiring and G a nonzero Jordan ideal of S. If S is noncommutative such that for all u,vG and rS

    a[r,uv]b=0, (2.1)

    then a=0 or b=0.

    Proof. In (2.1) replacing r by ar and using MA-semiring identities, we obtain

    aa[r,uv]b+a[a,uv]rb=0 (2.2)

    Using (2.1) again, we get a[a,uv]Sb=0. By the primeness of S, we have either b=0 or a[a,uv]=0. Suppose that

    a[a,uv]=0 (2.3)

    In view of Lemma 1, replacing v by 2v[s,t] in (2.3) and using 2-torsion freeness of S, we get 0=a[a,uv[s,t]]=auv[a,[s,t]]+a[a,uv][s,t]. Using (2.3) again auv[a,[s,t]]=0 and therefore auG[a,[s,t]]={0}. By the Lemma 2, we have either aG={0} or [a,[s,t]]=0. By Remark 1, aG={0} implies a=0. Suppose that

    [a,[s,t]]=0 (2.4)

    In (2.4) replacing s by sa, we get [a,s[a,t]]+[a,[s,t]a]=0 and therefore [a,s[a,t]]+[a,[s,t]]a=0. Using (2.4) again, we get [a,s][a,t]=0. By the primeness of S, [a,s]=0 and therefore aZ(S). Hence from (2.2), we can write aS[r,uv]b={0}. By the primeness of S, we obtain a=0 or

    [r,uv]b=0 (2.5)

    In (2.5) replacing r by rs and using (2.5) again, we get [r,uv]Sb={0}. By the primeness of S, we have either b=0 or [r,uv]=0. Suppose that

    [r,uv]=0 (2.6)

    In (2.6) replacing y by 2v[s,t] and using (2.6) again, we obtain 2[r,uv[s,t]]=0. As S is 2-torsion free, [r,uv[s,t]]=0 which further gives uG[r,[s,t]]={0}. As G{0}, by Lemma 2 [r,[s,t]]=0 which shows that S is commutative, a contradiction. Hence we conclude that a=0 or b=0.

    Theorem 1. Let S be a 2-torsion free prime MA-semiring and G a nonzero Jordan ideal of S. If d1 and d2 are derivations of S such that for all uG,

    d1d2(u)=0 (2.7)

    then either d1=0 or d2=0.

    Proof. Suppose that d20. We will show that d1=0. In view of Lemma 1, replacing u by 4u2v,vG in (2.7), we obtain d1d2(4u2v)=0 and by the 2-torsion freeness of S, we have d1d2(u2v)=0. Using (2.7) again, we obtain

    d2(u2)d1(v)+d1(u2)d2(v)=0 (2.8)

    By lemma 1, replacing v by 2[r,jk]v,j,kG in (2.8), we get

    d2(u2)d1(2[r,jk]v)+d1(u2)d2(2[r,jk]v)=0

    and

    2d2(u2)[r,jk]d1(v)+2d2(u2)d1([r,jk])v+2d1(u2)[r,jk]d2(v)+2d1(u2)d2([r,jk])v=0

    Using (2.8) again and hence by the 2-torsion freeness of S, we obtain

    d2(u2)[r,jk]d1(v)+d1(u2)[r,jk]d2(v)=0 (2.9)

    In (2.9), replacing v by 4v2t,tS and using (2.9) again, we obtain

    4d2(u2)[r,jk]v2d1(t)+4d1(u2)[r,jk]v2d2(t)=0

    As S is 2-torsion free, therefore

    d2(u2)[r,jk]v2d1(t)+d1(u2)[r,jk]v2d2(t)=0 (2.10)

    In (2.10), taking t=d2(g),gG and using (2.7), we obtain

    d1(u2)[r,jk]v2d2(d2(g))=0 (2.11)

    In (2.11) writing a for d1(u2) and b for v2d2(d2(g)), we have a[r,jk]b=0,rS,j,kG.

    Firstly suppose that S is not commutative. By Lemma 5, we have a=0 or b=0. If d1(u2)=a=0, then by Lemma 3, d1=0. Secondly suppose that S is commutative. In (2.7) replacing u by 2u2, we obtain 0=d1d2(2u2)=2d1d2(u2)=4d1(ud2(u))=4(d1(u)d2(u)+ud1d2(u)). Using (2.7) and the 2-torsion freeness of S, we obtain d1(u)d2(u)=0. By our assumption S is commutative, therefore d1(u)Sd2(u)={0}. By the primeness of S, we have either d1(G)={0} or d2(G)={0}. By Theorem 2.4 of [11], we have d1=0 or d2=0. But d20. Hence d1=0 which completes the proof.

    Theorem 2. Let S be a 2-torsion free prime MA-semiring and G a nonzero Jordan ideal of S. If d1 and d2 are derivations of S such that for all uG

    d1(d2(u)+u)=0, (2.12)

    then d1=0.

    Proof. Firstly suppose that S is commutative. Replacing u by 2u2 in (2.12) and using (2.12) again, we obtain d1(u)d2(u)=0 which further implies d1(u)Sd2(u)={0}. In view of Theorem 2.4 of [11], by the primeness of S we have d1=0 or d2=0. If d2=0, then from (2.12), we obtain d1(u)=0,uG and hence by Lemma 3, we conclude d1=0. Secondly suppose that S is noncommutative. Further suppose that d20. We will show that d1=0. In (2.12) replacing u by 4u2v,vG, and using (2.12) again, we obtain 2(d2(u2)d1(v)+d1(u2)d2(v))=0. As S is 2-torsion free, therefore

    d2(u2)d1(v)+d1(u2)d2(v)=0 (2.13)

    In (2.13) replacing v by 2[r,jk]v,rS,j,k,vG, we obtain

    d2(u2)d1(2[r,jk])v+2d2(u2)[r,jk]d1(v)+d1(u2)d2(2[r,jk])v+2d1(u2)[r,jk]d2(v)=0

    As by MA-semiring identities, 2[r,jk]=2j[r,k]+2[r,j]k, by Lemma 1 2[r,jk]G. Therefore using (2.13) again and the 2-torsion freeness of S, we obtain

    d2(u2)[r,jk]d1(v)+d1(u2)[r,jk]d2(v)=0 (2.14)

    In (2.14) replacing v by 4v2t,tS and using (2.14) again, we get

    d2(u2)[r,jk]v2d1(t)+d1(u2)[r,jk]v2d2(t)=0 (2.15)

    In (2.15) taking t=t(d2(w)+w),wG, we get

    d2(u2)[r,jk]v2d1(t(d2(w)+w))+d1(u2)[r,jk]v2d2(t(d2(w)+w))=0

    and therefore

    d2(u2)[r,jk]v2d1(t)(d2(w)+w)+d2(u2)[r,jk]v2td1((d2(w)+w))

    +d1(u2)[r,jk]v2d2(t)(d2(w)+w)+d1(u2)[r,jk]v2td2(d2(w)+w)=0

    Using (2.12) and (2.15) in the last expression, we obtain

    (d1(u2))[r,jk](v2td2(d2(w)+w))=0 (2.16)

    Applying Lemma 5 on (2.15), we get either d1(u2)=0 or v2td2(d2(w)+w)=0. If d1(u2)=0 then by Lemma 3, d1=0. If v2Sd2(d2(w)+w)={0}, the by the primeness of S, we have v2=0 or d2(d2(w)+w)=0. If v2=0,vG, then G={0}, a contradiction. Suppose that for all wG

    d2(d2(w)+w)=0 (2.17)

    In (2.17)replacing w by 4z2u,z,uG, and using (2.17) again, we obtain

    d2(z2)d2(u)=0 (2.18)

    In (2.18), replacing u by 4xz2,xG and using (2.18) again, we obtain d2(z2)Gd2(z2)={0}. By Lemma 2, d2(z2)=0 and hence by Lemma 3, we conclude that d2=0. Taking d2=0 in the hypothesis to obtain d1(u)=0 and hence by Theorem 2.4 of [11], we have d1=0.

    Theorem 3. Let G be a nonzero Jordan ideal of a 2-torsion free prime MA-semiring S and d1 and d2 be derivations of S such that for all u,vG

    [d1(u),d2(v)]+[u,v]=0 (2.19)

    Then S is commutative.

    Proof. If d1=0 or d2=0, then from (2.19), we obtain [G,G]={0}. By Theorem 2.3 of [11] S is commutative. We assume that both d1 and d2 are nonzero. In (2.19) replacing u by 4uw2 and using MA-semiring identities and 2-torsion freeness of S, we get

    d1(u)[2w2,d2(v)]+([d1(u),d2(v)]+[u,v])2w2+u([d1(2w2),d2(v)]

    +[2w2,v])+[u,d2(v)]d1(2w2)=0

    Using (2.19) again, we get

    d1(u)[2w2,d2(v)]+[u,d2(v)]d1(2w2)=0

    and by the 2-torsion freeness of S, we have

    d1(u)[w2,d2(v)]+[u,d2(v)]d1(w2)=0 (2.20)

    Replacing u by 2u[r,jk] in (2.20) and using it again, we obtain

    d1(u)[r,jk][w2,d2(v)]+[u,d2(v)][r,jk]d1(w2)=0 (2.21)

    In (2.21) replacing u by 4su2 and using (2.21) again, we obtain

    d1(s)u2[r,jk][w2,d2(v)]+[s,d2(v)]u2[r,jk]d1(w2)=0 (2.22)

    In (2.22) replacing s by d2(v)s and then using commutator identities, we get

    d1d2(v)su2[r,jk][w2,d2(v)]=0 (2.23)

    Therefore d1d2(v)Su2[r,jk][w2,d2(v)]={0}. By the primeness of S, we obtain either d1d2(v)=0 or u2[r,jk][w2,d2(v)]=0. Consider the sets

    G1={vG:d1d2(v)=0}

    and

    G2={vG:u2[r,jk][w2,d2(v)=0}

    We observe that G=G1G2. We will show that either G=G1 or G=G2. Suppose that v1G1G2 and v2G2G1. Then v1+v2G1+G2G1G2=G. We now see that 0=d1d2(v1+v2)=d1d2(v2), which shows that v2G1, a contradiction. On the other hand 0=u2[r,jk][w2,d2(v1+v2)]=u2[r,jk][w2,d2(v1)], which shows that v1G2, a contradiction. Therefore either G1G2 or G2G1, which respectively show that either G=G1 or G=G2. Therefore we conclude that for all vG, d1d2(v)=0 or u2[r,jk][w2,d2(v)]=0. Suppose that d1d2(v)=0,vG. then by Lemma 2.1, d1=0 or d2=0. Secondly suppose that u2[r,jk][w2,d2(v)]=0,u,v,w,j,kG,rS. By Lemma 5, we have either u2=0 or [w2,d2(v)]=0. But u2=0 leads to G={0} which is not possible. Therefore [w2,d2(v)]=0 and employing Lemma 4, [d2(v),s]=0,sS. Hence by Theorem 2.2 of [22], S is commutative.

    Theorem 4. Let G be a nonzero Jordan ideal of a 2-torsion free prime MA-semiring S and d1 and d2 be derivations of S such that for all u,vG

    d1(u)d2(v)+[u,v]=0 (2.24)

    Then d1=0 or d2=0 and thus S is commutative.

    Proof. It is quite clear that if at least one of d1 and d2 is zero, then we obtain [G,G]={0}. By Theorem 2.3 of [11] and Theorem 2.1 of [22], S is commutative. So we only show that at least one of the derivations is zero. Suppose that d20. In (2.24), replacing v by 4vw2,wG, we obtain d1(u)d2(4vw2)+[u,4vw2]=0 and therefore using MA-semirings identities, we can write

    4d1(u)vd2(w2)+4d1(u)d2(v)w2+4v[u,w2]+4[u,v]w2=0

    In view of Lemma 1 as 2w2G, using (2.24) and the 2-torsion freeness of S, we obtain

    d1(u)vd2(w2)+v[u,w2]=0 (2.25)

    In (2.25) replacing v by 2[s,t]v, s,tS and hence by the 2-torsion freeness of S, we get

    d1(u)[s,t]vd2(w2)+[s,t]v[u,w2]=0 (2.26)

    Multiplying (2.25) by [s,t] from the left, we get

    [s,t]d1(u)vd2(w2)+[s,t]v[u,w2]=0

    and since S is an MA-semiring, therefore

    [s,t]d1(u)vd2(w2)=[s,t]v[u,w2] (2.27)

    Using (2.27) into (2.26), we obtain d1(u)[s,t]vd2(w2)+[s,t]d1(u)vd2(w2)=0. By MA-semirings identities, we further obtain [d1(u),[s,t]]Gd2(w2)={0}. By Lemma 2, we obtain either [d1(u),[s,t]]=0 or d2(w2)=0. If d2(w2)=0, then by Lemma 3, d2=0. On the other hand, if

    [d1(u),[s,t]]=0 (2.28)

    In (2.28) replacing t by st, we get [d1(u),s[s,t]]=0 and using (2.23) again [d1(u),s][s,t]=0 and therefore [d1(u),s]S[s,t]={0} and by the primeness of S, we get [S,S]={0} and hence S is commutative or [d1(u),s]=0. In view of Theorem 2.2 of [22] from [d1(u),s]=0 we have [S,S]={0} which further implies S is commutative. Hence (2.19)becomes d1(u)d2(v)=0. As above we have either d1=0 or d2=0.

    Theorem 5. Let S be a 2-torsion free prime MA-semiring and G a nonzero Jordan ideal of S. If d1, d2 and d3 be nonzero. derivations such that for all u,vG either

    1). d3(v)d1(u)+d2(u)d3(v)=0 or

    2). d3(v)d1(u)+d2(u)d3(v)+[u,v]=0.

    Then S is commutative and d1=d2.

    Proof. 1). By the hypothesis for the first part, we have

    d3(v)d1(u)+d2(u)d3(v)=0 (2.29)

    which further implies

    d3(v)d1(u)=d2(u)d3(v) (2.30)

    In (2.29) replacing u by 4uw2, we obtain

    4d3(v)d1(u)w2+4d3(v)ud1(w2)+4d2(u)w2d3(v)+4ud2(w2)d3(v)=0

    and therefore by the 2-torsion freeness of S, we have

    d3(v)d1(u)w2+d3(v)ud1(w2)+d2(u)w2d3(v)+ud2(w2)d3(v)=0 (2.31)

    Using (2.30) into (2.31), we obtain

    d2(u)[d3(v),w2]+[d3(v),u]d1(w2)=0 (2.32)

    In (2.32) replacing u by 2u[r,jk],rS,j,kG, and using (2.32) again, we get

    d2(u)[r,jk][d3(v),w2]+[d3(v),u][r,jk]d1(w2)=0 (2.33)

    In (2.33) replacing u by 4tu2,tS and using 2-torsion freeness and (2.33) again, we get

    d2(t)u2[r,jk][d3(v),w2]+[d3(v),t]u2[r,jk]d1(w2)=0 (2.34)

    Taking t=d3(v)t in (2.34) and using (2.34) again we obtain

    d2d3(v)tu2[r,jk][d3(v),w2]=0 (2.35)

    We see that equation (2.35) is similar as (2.23) of the previous theorem, therefore repeating the same process we obtain the required result.

    2). By the hypothesis, we have

    d3(v)d1(u)+d2(u)d3(v)+[u,v]=0 (2.36)

    For d3=0, we obtain [G,G]={0} and by Theorem 2.3 of [11] this proves that S is commutative. Assume that d30. From (2.36), using MA-semiring identities, we can write

    d3(v)d1(u)=d2(u)d3(v)+[u,v] (2.37)

    and

    d3(v)d1(u)+[u,v]=d2(u)d3(v) (2.38)

    In (2.36), replacing u by 4uz2, we obtain

    4(d3(v)ud1(z2)+d3(v)d1(u)z2+d2(u)z2d3(v)+ud2(z2)d3(v)+u[z2,v])+[u,v]z2)=0

    and using (2.37) and (2.38) and then 2-torsion freeness of S, we obtain

    [d3(v),u]d1(z2)+d2(u)[d3(v),z2]=0 (2.39)

    We see that (2.39) is same as (2.32) of the previous part of this result. This proves that [S,S]={0} and hence S is commutative. Also then by the hypothesis, since d30, d1=d2.

    Theorem 6. Let G be nonzero Jordan ideal of a 2-torsion free prime MA-semiring S and d1 and d2 be nonzero derivations of S such that for all u,vG

    [d2(v),d1(u)]+d1[v,u]=0 (2.40)

    Then S is commutative.

    In (2.40) replacing u by 4uw2,wG and using 2-torsion freeness and again using(2.40), we obtain

    [d2(v)+v,u]d1(w2)+d1(u)[d2(v)+v,w2]=0 (2.41)

    In (2.41) replacing u by 2u[r,jk],j,kG,rS, we obtain

    u[d2(v)+v,2[r,jk]]d1(w2)+2[d2(v)+v,u][r,jk]d1(w2)

    +ud1(2[r,jk])[d2(v)+v,w2]+2d1(u)[r,jk][d2(v)+v,w2]=0

    Using 2-torsion freeness and (2.41) again, we get

    [d2(v)+v,u][r,jk]d1(w2)+d1(u)[r,jk][d2(v)+v,w2]=0 (2.42)

    In(2.42) replacing u by 4tu2,tSand using (2.42) again, we get

    [d2(v)+v,t]u2[r,jk]d1(w2)+d1(t)u2[r,jk][d2(v)+v,w2]=0 (2.43)

    In (2.43) taking t=(d2(v)+v)t and using MA-semirings identities, we obtain

    (d2(v)+v)[d2(v)+v,t]u2[r,jk]d1(w2)+d1(d2(v)+v)tu2[r,jk][d2(v)+v,w2]

    +(d2(v)+v)d1(t)u2[r,jk][d2(v)+v,w2]=0

    and using (2.43) again, we obtain

    d1(d2(v)+v)tu2[r,jk][d2(v)+v,w2]=0 (2.44)

    By the primeness we obtain either d1(d2(v)+v)=0 or u2[r,jk][d2(v)+v,w2]=0. If d1(d2(v)+v)=0, then by Theorem 2 we have d1=0, which contradicts the hypothesis. Therefore we must suppose u2[r,jk][d2(v)+v,w2]=0. By Lemma 5, we have either u2=0 or [d2(v)+v,w2]=0. But u2=0 implies G={0} which is not possible. On the other hand applying Lemma 5, we have [d2(v)+v,r]=0,rS and therefore taking r=v,vG and [d2(v),v]+[v,v]=0 and using MA-semiring identities, we get

    [d2(v),v]+[v,v]=0 (2.45)

    As [v,v]=[v,v], from (2.45), we obtain [d2(v),v]+[v,v]=0 and therefore

    [d2(v),v]=[v,v] (2.46)

    Using (2.46) into (2.45), we get 2[d2(v),v]=0 and by the 2-torsion freeness of S, we get [d2(v),v]=0. By Theorem 2.2 of [22], we conclude that S is commutative.

    Corollary 1. Let G be nonzero Jordan ideal of a 2-torsion free prime MA-semiring S and d be a nonzero derivation of S such that for all u,vG d[v, u] = 0. Then S is commutative

    Proof. In theorem (6) taking d2=0 and d1=d, we get the required result.

    Theorem 7. Let G be a nonzero Jordan ideal of a 2-torsion free prime MA-semiring and d2 be derivation of S. Then there exists no nonzero derivation d1 such that for all u,vG

    d2(v)d1(u)+d1(vu)=0 (2.47)

    Proof. Suppose on the contrary that there is a nonzero derivation d1 satisfying (2.47). In (2.47) replacing u by 4uw2,wG and using (2.47) again, we obtain

    d1(u)[w2,d2(v)+v]+[u,d2(v)]d1(w2)+ud1(vw2)+(uv)d1(w2)+ud1[v,w2]=0 (2.48)

    In (2.48), replacing u by u[r,jk],rS,j,kG and using (2.48) again, we get

    d1(u)[r,jk][w2,d2(v)+v]+[u,d2(v)+v][r,jk]d1(w2)=0 (2.49)

    In (2.49) replacing u by 4tu2,tS and using (2.49) again, we obtain

    d1(t)u2[r,jk][w2,d2(v)+v]+td1(u2)[r,jk][w2,d2(v)+v]

    +t[u2,d2(v)+v][r,jk]d1(w2)+[t,d2(v)+v]u2[r,jk]d1(w2)=0

    and using2-torsion freeness and (2.49) again, we obtain

    d1(t)u2[r,jk][w2,d2(v)+v]+[t,d2(v)+v]u2[r,jk]d1(w2)=0 (2.50)

    In (2.50) taking t=(d2(v)+v)t and using MA-semirings identities, we get d1(d2(v)+v)tu2[r,jk][w2,d2(v)+v]+(d2(v)+v)d1(t)u2[r,jk][w2,d2(v)+v]

    +(d2(v)+v)[t,d2(v)+v]u2[r,jk]d1(w2)=0

    Using (2.50) again, we obtain

    d1(d2(v)+v)tu2[r,jk][w2,d2(v)+v]=0 (2.51)

    that is d1(d2(v)+v)Su2[r,jk][w2,d2(v)+v]=0. Therefore by the primeness following the same process as above, we have either d1(d2(v)+v)=0 or u2[r,jk][w2,d2(v)+v]=0 for all u,v,j,k,wG,rS. If d1(d2(v)+v)=0. As d10, therefore d2(v)+v=0. Secondly suppose that u2[r,jk][w2,d2(v)+v]=0. By Lemma 5, we have either u2=0 or [w2,d2(v)+v]=0. But u2=0 implies that G={0}, a contradiction. Therefore we consider the case when [w2,d2(v)+v]=0, which implies, by Lemma 4, that [d2(v)+v,r]=0,rS and taking in particular t=vG, we have

    [d2(v),v]+[v,v]=0 (2.52)

    Also by definition of MA-semirings, we have [v,v]=[v,v]. Therefore [d2(v),v]+[v,v]=0 and therefore

    [d2(v),v]=[v,v] (2.53)

    Using (2.53) into (2.52) and then using 2-torsion freeness of S, we obtain [d(v),v]=0. By Theorem 2.2 of [22], we conclude that S is commutative. Therefore (2.47) will be rewritten as 2d1(u)d2(v)+2(d1(v)u+vd1(u))=0 and hence by the 2-torsion freeness of S, we obtain

    d1(u)d2(v)+d1(v)u+vd1(u)=0 (2.54)

    In (2.54) replacing u by 2uw and using 2-torsion freeness of S, we get

    d1(u)wd2(v)+ud1(w)d2(v)+d1(v)uw+vd1(u)w+vud1(w)=0

    and therefore

    w(d1(u)d2(v)+d1(v)u+vd1(u))+ud1(w)d2(v)+vud1(w)=0

    Using (2.54) again, we obtain

    ud1(w)d2(v)+vud1(w)=0 (2.55)

    In (2.55) replacing v by 2vz, we get

    ud1(w)d2(v)z+ud1(w)vd2(z)+vzud1(w)=0

    and therefore

    z(ud1(w)d2(v)+vud1(w))+ud1(w)vd2(z)=0

    and using (2.55) again, we get d1(w)uGd2(z)={0}. By the above Lemma 2, we have either d1(w)u=0 or d2(z)=0 and therefore by Remark 1, we have either d1(w)=0 or d2(z)=0. As d10, therefore d2=0. Therefore our hypothesis becomes d1(uv)=0 and therefore d1(u2)=0, uG. By Lemma 3, d1=0 a contraction to the assumption. Hence d1 is zero.

    We have proved the results of this paper for prime semirings and it would be interesting to generalize them for semiprime semirings, we leave it as an open problem.

    Taif University Researchers Supporting Project number (TURSP-2020/154), Taif University Taif, Saudi Arabia.

    The authors declare that they have no conflict of interest.



    [1] C. Kolberg-Liedtke, F. Feuerhake, M. Garke, M. Christgen, R. Kates, E. M. Grischke, et al., Impact of stromal tumor-infiltrating lymphocytes (sTILs) on response to neoadjuvant chemotherapy in triple-negative early breast cancer in the WSG-ADAPT TN trial, Breast Cancer Res., 24 (2022), 1–13. https://doi.org/10.1186/s13058-022-01552-w doi: 10.1186/s13058-022-01552-w
    [2] T. Nguyen, M. V. Ngo, V. P. Nguyen, Histopathological imaging classification of breast tissue for cancer diagnosis support using deep learning models, in International Conference on Industrial Networks and Intelligent Systems, 444 (2022), 152–164. https://doi.org/10.1007/978-3-031-08878-0_11
    [3] G. Floris, G. Broeckx, A. Antoranz, M. D. Schepper, R. Salgado, C. Desmedt, et al., Tumor infiltrating lymphocytes in breast cancer: Implementation of a new histopathological biomarker, in Biomarkers of the Tumor Microenvironment, Springer, (2022), 207–243. https://doi.org/10.1007/978-3-030-98950-7_13
    [4] H. Kuroda, T. Jamiyan, R. Yamaguchi, A. Kakumoto, A. Abe, O. Harada, et al., Tumor microenvironment in triple-negative breast cancer: The correlation of tumor-associated macrophages and tumor-infiltrating lymphocytes, Clin. Transl. Oncol., 23 (2021), 2513–2525. https://doi.org/10.1007/s12094-021-02652-3 doi: 10.1007/s12094-021-02652-3
    [5] T. Odate, M. K. Le, M. Kawai, M. Kubota, Y. Yamaguchi, T. Kondo, Tumor-infiltrating lymphocytes in breast FNA biopsy cytology: A predictor of tumor-infiltrating lymphocytes in histologic evaluation, Cancer Cytopathol., 130 (2022), 336–343. https://doi.org/10.1002/cncy.22551 doi: 10.1002/cncy.22551
    [6] S. Wang, J. Sun, K. Chen, P. Ma, N. Li, Perspectives of tumor-infiltrating lymphocyte treatment in solid tumors, BMC Med., 140 (2021), 1–7. https://doi.org/10.1186/s12916-021-02006-4 doi: 10.1186/s12916-021-02006-4
    [7] Y. Li, Z. Yang, Y. Wang, X. Cao, X. Xu, A neural network approach to analyze cross-sections of muscle fibers in pathological images, Comput. Biol. Med., 104 (2019), 97–104. https://doi.org/10.1016/j.compbiomed.2018.11.007 doi: 10.1016/j.compbiomed.2018.11.007
    [8] X. Wu, Y. Zheng, C. H. Chu, L. Cheng, J. Kim, Applying deep learning technology for automatic fall detection using mobile sensors, Biomed. Signal Process. Control, 72 (2022), 103355. https://doi.org/10.1016/j.bspc.2021.103355 doi: 10.1016/j.bspc.2021.103355
    [9] J. Cheng, S. Tian, L. Yu, C. Gao, X. Kang, X. Ma, et al., ResGANet: Residual group attention network for medical image classification and segmentation, Med. Image Anal., 76 (2022), 102313. https://doi.org/10.1016/j.media.2021.102313 doi: 10.1016/j.media.2021.102313
    [10] D. Müller, I. Soto-Rey, F. Kramer, An analysis on ensemble learning optimized medical image classification with deep convolutional neural networks, IEEE Access, 10 (2022), 66467–66480. https://doi.org/10.1109/ACCESS.2022.3182399 doi: 10.1109/ACCESS.2022.3182399
    [11] W. Pinaya, P. D. Tudosiu, R. Gray, G. Rees, P. Nachev, S. Ourselin, et al., Unsupervised brain imaging 3d anomaly detection and segmentation with transformers, Med. Image Anal., 79 (2022), 102475. https://doi.org/10.1016/j.media.2022.102475 doi: 10.1016/j.media.2022.102475
    [12] S. Javed, A. Mahmood, J. Dias, N. Werghi, N. Rajpoot, Spatially constrained context-aware hierarchical deep correlation filters for nucleus detection in histology images, Med. Image Anal., 72 (2021), 102104. https://doi.org/10.1016/j.media.2021.102104 doi: 10.1016/j.media.2021.102104
    [13] Z. Tan, J. Feng, J. Zhou, SGNet: Structure-aware graph-based network for airway semantic segmentation, in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2021), 153–163. https://doi.org/10.1007/978-3-030-87193-2_15
    [14] Mehdi, S. Örjan, W. Chunliang, Prior-aware autoencoders for lung pathology segmentation, Med. Image Anal., 80 (2022), 102491. https://doi.org/10.1016/j.media.2022.102491 doi: 10.1016/j.media.2022.102491
    [15] T. Vicar, J. Chmelik, R. Kolar, Cell segmentation in quantitative phase images with improved iterative thresholding method, in European Medical and Biological Engineering Conference, (2020), 233–239. https://doi.org/10.1007/978-3-030-64610-3_27
    [16] M. Gamarra, E. Zurek, H. J. Escalante, L. Hurtado, H. San-Juan-Vergara, Split and merge watershed: A two-step method for cell segmentation in fluorescence microscopy images, Biomed. Signal Process. Control, 53 (2019), 101575. https://doi.org/10.1016/j.bspc.2019.101575 doi: 10.1016/j.bspc.2019.101575
    [17] E. Shelhamer, J. Long, T. Darrell, Fully convolutional networks for semantic segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 39 (2017), 640–651. https://doi.org/10.1109/TPAMI.2016.2572683 doi: 10.1109/TPAMI.2016.2572683
    [18] O. Ronneberger, P. Fischer, T. Brox, U-Net: Convolutional networks for biomedical image segmentation, in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, (2015), 234–241. https://doi.org/10.1007/978-3-319-24574-4_28
    [19] L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in Proceedings of the European Conference on Computer Vision, 11211 (2018), 833–851. https://doi.org/10.1007/978-3-030-01234-2_49
    [20] C. E. Akbas, M. Kozubek, Condensed U-Net (Cu-Net): An improved u-net architecture for cell segmentation powered by 4×4 max-pooling layers, in Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), (2020), 446–450. https://doi.org/10.1109/ISBI45749.2020.9098351
    [21] C. E. Akbaş, M. Kozubek, Weakly supervised multi-task learning for cell detection and segmentation, in Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), (2020), 513–516. https://doi.org/10.1109/ISBI45749.2020.9098518
    [22] X. Zhang, X. Zhu, K. Tang, Y. Zhao, Z. Lu, Q. Feng, DDTNet: A dense dual-task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer, Med. Image Anal., 78 (2022), 102415. https://doi.org/10.1016/j.media.2022.102415 doi: 10.1016/j.media.2022.102415
    [23] H. Wang, Y. Jiang, B. Li, Y. Cui, R. Li, Single-cell spatial analysis of tumor and immune microenvironment on whole-slide image reveals hepatocellular carcinoma subtypes, Cancers, 12 (2020), 3562. https://doi.org/10.3390/cancers12123562 doi: 10.3390/cancers12123562
    [24] E. Budginait, M. A. Morkūnas, Laurinaviius, P. Treigys, Deep learning model for cell nuclei segmentation and lymphocyte identification in whole slide histology images, Informatica, 1 (2021), 1–18. https://doi.org/10.15388/20-INFOR442 doi: 10.15388/20-INFOR442
    [25] J. Li, K. Jin, D. Zhou, N. Kubota, Z. Ju, Attention mechanism-based cnn for facial expression recognition, Neurocomputing, 411 (2020). https://doi.org/10.1016/j.neucom.2020.06.014 doi: 10.1016/j.neucom.2020.06.014
    [26] Z. Li, Z. Peng, S. Tang, C. Zhang, H. Ma, Text summarization method based on double attention pointer network, IEEE Access, 8 (2020). 11279–11288. https://doi.org/10.1109/ACCESS.2020.2965575 doi: 10.1109/ACCESS.2020.2965575
    [27] H. Jie, S. Li, S. Gang, Squeeze-and-excitation networks, in Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2018), 7132–7141. https://doi.org/10.1109/CVPR.2018.00745
    [28] W. Fei, M. Jiang, Q. Chen, S. Yang, X. Tang, Residual attention network for image classification, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), 6450–6458. https://doi.org/10.1109/CVPR.2017.683
    [29] C. Yin, S. Liu, R. Shao, P. C. Yuen, Focusing on clinically interpretable features: selective attention regularization for liver biopsy image classification, in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 12905 (2021), 153–162. https://doi.org/10.1007/978-3-030-87240-3_15
    [30] Y. Gao, M. Zhou, D. Metaxas, UTNet: A hybrid transformer architecture for medical image segmentation, in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 12903 (2021), 61–71. https://doi.org/10.1007/978-3-030-87199-4_6
    [31] Z. Zhong, Z. Q. Lin, R. Bidart, X. Hu, A. Wong, Squeeze-and-attention networks for semantic segmentation, in Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2020), 13062–13071. https://doi.org/10.1109/CVPR42600.2020.01308
    [32] T. Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, S. Belongie, Feature pyramid networks for object detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), 936–944. https://doi.org/10.1109/CVPR.2017.106
    [33] Z. Zhou, M. Siddiquee, N. Tajbakhsh, J. Liang, Unet++: Redesigning skip connections to exploit multiscale features in image segmentation, IEEE Trans. Med. Imaging, 39 (2020), 1856–1867. https://doi.org/10.1109/TMI.2019.2959609 doi: 10.1109/TMI.2019.2959609
    [34] H. Huang, L. Lin, R. Tong, H. Hu, J. Wu, UNet 3+: A full-scale connected unet for medical image segmentation, in Proceedings of the ICASSP 2020-2020 IEEE International Conference on Acoustics, (2020), 1055–1059. https://doi.org/10.1109/ICASSP40776.2020.9053405
    [35] K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2016), 770–778. https://doi.org/10.1109/CVPR.2016.90
    [36] C. Zhao, M. Hu, F. Ju, Z. Chen, Y. Li, Y. Feng, Convolutional neural network with spatio-temporal-channel attention for remote heart rate estimation, Visual Comput., 2022 (2022), 1–19. https://doi.org/10.1007/s00371-022-02624-w doi: 10.1007/s00371-022-02624-w
    [37] A. Janowczyk, A. Madabhushi, Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases, J. Pathol. Inf., 7 (2016), 1–18. https://doi.org/10.4103/2153-3539.186902 doi: 10.4103/2153-3539.186902
    [38] V. Badrinarayanan, A. Kendall, R. Cipolla, Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans. Pattern Anal. Mach. Intel., 39 (2017), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615 doi: 10.1109/TPAMI.2016.2644615
    [39] A. Paszke, A. Chaurasia, S. Kim, E. Culurciello, Enet: A deep neural network architecture for real-time semantic segmentation, preprint, arXiv: 1606.02147. https://doi.org/10.48550/arXiv.1606.02147
    [40] M. Z. Alom, C. Yakopcic, M. Hasan, T. M. Taha, V. K. Asari, Recurrent residual u-net for medical image segmentation, J. Med. Imaging, 6 (2019), 1–16. https://doi.org/10.1117/1.JMI.6.1.014006 doi: 10.1117/1.JMI.6.1.014006
    [41] Y. Wu, W. Cao, Y. Liu, Z. Ming, J. Li, B. Lu, Semantic auto-encoder with l2-norm constraint for zero-shot learning, in 2021 13th International Conference on Machine Learning and Computing, (2021), 101–105. https://doi.org/10.1145/3457682.3457699
    [42] F. Li, Y. Zhao, Y. Wei, Y. Xi, H. Bu, Tumor-infiltrating lymphocytes improve magee equation–based prediction of pathologic complete response in HR-Positive/HER2-Negative breast cancer, Am. J. Clin. Oncol., 158 (2022), 291–299. https://doi.org/10.1093/ajcp/aqac041 doi: 10.1093/ajcp/aqac041
    [43] K. M. Ratheesh, L. K. Seah, V. M Murukeshan, Spectral phase-based automatic calibration scheme for swept source-based optical coherence tomography systems, Phys. Med. Biol., 61 (2016) 7652–7663. https://doi.org/10.1088/0031-9155/61/21/7652 doi: 10.1088/0031-9155/61/21/7652
    [44] R. K. Meleppat, C. R. Fortenbach, Y. Jian, K. Wagner, B. S. Modjtahedi, M. J. Motta, et al., In Vivo imaging of retinal and choroidal morphology and vascular plexuses of vertebrates using swept-source optical coherence tomography, Transl. Vision Sci. Technol., 11 (2022), 1–21. https://doi.org/10.1167/tvst.11.8.11 doi: 10.1167/tvst.11.8.11
    [45] P. Udayaraju, P. Jeyanthi, Early diagnosis of age-related macular degeneration (ARMD) using deep learning, Intell. Syst. Sustainable Comput., 289 (2022), 657–663. https://doi.org/10.1007/978-981-19-0011-2_59. doi: 10.1007/978-981-19-0011-2_59
  • This article has been cited by:

    1. Tariq Mahmood, Liaqat Ali, Muhammad Aslam, Ghulam Farid, On commutativity of quotient semirings through generalized derivations, 2023, 8, 2473-6988, 25729, 10.3934/math.20231312
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2560) PDF downloads(116) Cited by(8)

Figures and Tables

Figures(7)  /  Tables(3)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog