7 papers
Self-Supervised Learning for Knee Osteoarthritis: Diagnostic Limitations and Prognostic Value of Hospital Data
Haresh Rengaraj Rajamohan, Yuxuan Chen, Kyunghyun Cho +1
This study assesses whether self-supervised learning (SSL) improves knee osteoarthritis (OA) modeling for diagnosis and prognosis relative to ImageNet-pretrained initialization. We…
StainNet: Scaling Self-Supervised Foundation Models on Immunohistochemistry and Special Stains for Computational Pathology
Jiawen Li, Jiali Hu, Xitong Ling +6
Foundation models trained with self-supervised learning (SSL) on large-scale histological images have significantly accelerated the development of computational pathology. These mo…
Deformable Attention Graph Representation Learning for Histopathology Whole Slide Image Analysis
Mingxi Fu, Xitong Ling, Yuxuan Chen +6
Accurate classification of Whole Slide Images (WSIs) and Regions of Interest (ROIs) is a fundamental challenge in computational pathology. While mainstream approaches often adopt M…
An Inclusive Foundation Model for Generalizable Cytogenetics in Precision Oncology
Changchun Yang, Weiqian Dai, Yilan Zhang +8
Chromosome analysis is vital for diagnosing genetic disorders and guiding cancer therapy decisions through the identification of somatic clonal aberrations. However, developing an…
Cross-Modal Prototype Allocation: Unsupervised Slide Representation Learning via Patch-Text Contrast in Computational Pathology
Yuxuan Chen, Jiawen Li, Jiali Hu +4
With the rapid advancement of pathology foundation models (FMs), the representation learning of whole slide images (WSIs) attracts increasing attention. Existing studies develop hi…
Multimodal Distillation-Driven Ensemble Learning for Long-Tailed Histopathology Whole Slide Images Analysis
Xitong Ling, Yifeng Ping, Jiawen Li +8
Multiple Instance Learning (MIL) plays a significant role in computational pathology, enabling weakly supervised analysis of Whole Slide Image (WSI) datasets. The field of WSI anal…