TSMIL:Transformer-based structured low-rank end-to-end multi-instance learning network for renal cell carcinoma classification in whole-slide imagesOA
The pathological classification of renal cell carcinoma(RCC)is a critical indicator of its accurate diagnosis,treatment,and prognosis.Pathologists typically focus on a single subtype when determining classifications,whereas existing multiple instance learning approaches often lack the global modeling of instance-level features and fail to capture contextual dependencies.When multiple instances with large semantic disparities are projected into the same latent space,structural information loss or the dilution of critical pathological features may occur,thereby limiting classification performance.To address these challenges,we propose an end-to-end Transformer-based structured low-rank multiple instance learning framework,termed TSMIL,for RCC classification.Specifically,we introduce the multilayer spatial feature module to enhance morphological feature representation and the structured low-rank block to embed high-dimensional features into a low-rank structure,effectively capturing contextual information and exploring the latent semantic potential of pathological features in a sparse representation space.Extensive experiments demonstrate that our proposed TSMIL achieves a mean accuracy of 92.98%and an AUC of 0.9818,outperforming other state-of-the-art methods.Overall,our framework exhibits superior practicality and robustness in RCC pathology grading tasks.
Xiaoliang Xu;Yusong Mao;Hao Cui;Yue Han;Xinwei Zhang;Pan Huang;Hu Chen;Sukun Tian;Peng He;Peng Feng
Key Laboratory of Optoelectronic Technology&Systems(Ministry of Education),Chongqing University,Chongqing 400044,ChinaKey Laboratory of Optoelectronic Technology&Systems(Ministry of Education),Chongqing University,Chongqing 400044,ChinaKey Laboratory of Optoelectronic Technology&Systems(Ministry of Education),Chongqing University,Chongqing 400044,ChinaKey Laboratory of Optoelectronic Technology&Systems(Ministry of Education),Chongqing University,Chongqing 400044,ChinaKey Laboratory of Optoelectronic Technology&Systems(Ministry of Education),Chongqing University,Chongqing 400044,ChinaCentre for Smart Health,School of Nursing,The Hong Kong Polytechnic University,Hong Kong SAR 999077,ChinaCenter of Digital Dentistry,Peking University School and Hospital of Stomatology&NHC Key Laboratory of Digital Stomatology(Key Laboratory of Digital Stomatology,Chinese Academy of Medical Sciences),Beijing 100081,ChinaCenter of Digital Dentistry,Peking University School and Hospital of Stomatology&NHC Key Laboratory of Digital Stomatology(Key Laboratory of Digital Stomatology,Chinese Academy of Medical Sciences),Beijing 100081,ChinaKey Laboratory of Optoelectronic Technology&Systems(Ministry of Education),Chongqing University,Chongqing 400044,ChinaKey Laboratory of Optoelectronic Technology&Systems(Ministry of Education),Chongqing University,Chongqing 400044,China
医药卫生
Renal cell carcinomaEnd-to-end multiple instance learningLow-rank learning networkPathological classification
《Intelligent Oncology》 2026 (2)
P.39-52,14
supported by the Natural Science Foundation of Chongqing,China(Grant No.:CSTB2025NSCQ-LZX0041)Beijing Natural Science Foundation(Grant No.:L242114)Fundamental Research Funds for the Central Universities(Grant No.:2023CDJKYJH085)the Non-Profit Central Research Institute Fund of Chinese Academy of Medical Sciences(Grant No.:2023-PT320-09).
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