4 papers
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology
Chen Shu, Chengyang Zhang, Boyu Fu +5
Weak bag-level labels are widely available in computational pathology, whereas reliable instance-level annotations for whole-slide images remain expensive and ambiguous. Weakly sup…
CorePath: A Breast-Specialized Pathology Foundation Model for Core Needle Biopsy Diagnosis and Risk-Controlled Report Generation
Ting Yin, Danning Li, Chen Shu +17
Breast core needle biopsy (CNB) is central to breast cancer diagnosis yet remains challenging because limited tissue sampling, lesion heterogeneity, and subtle morphologic overlap…
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning
Chen Shu, Boyu Fu, Yiman Li +5
Multiple Instance Learning is the predominant method for Whole Slide Image classification in digital pathology, enabling the use of slide-level labels to supervise model training.…
Classifying Long-tailed and Label-noise Data via Disentangling and Unlearning
Chen Shu, Mengke Li, Yiqun Zhang +4
In real-world datasets, the challenges of long-tailed distributions and noisy labels often coexist, posing obstacles to the model training and performance. Existing studies on long…