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Found 6 papers, 3 papers with code
Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learning
A series of out-of-distribution tests further confirmed the generality of our framework.
HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image
Many studies adopt random sampling pre-processing strategy and WSI-level aggregation models, which inevitably lose critical prognostic information in the patient-level bag.
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image
Moreover, we highlight AugDiff's higher-quality augmented feature over image augmentation and its superiority over self-supervised learning.
LNPL-MIL: Learning from Noisy Pseudo Labels for Promoting Multiple Instance Learning in Whole Slide Image
Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis analysis are promising directions in computational pathology.
Multiple Instance Learning with Mixed Supervision in Gleason Grading
We achieve the state-of-the-art performance on the SICAPv2 dataset, and the visual analysis shows the accurate prediction results of instance level.
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis.
