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Found 4 papers, 2 papers with code
In-context learning enables multimodal large language models to classify cancer pathology images
Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models.
Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology
Specifically, we analyzed the effects of adaptations in data volume, architecture, and algorithms on downstream classification tasks, emphasizing their impact on computational resources.
Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology
Deep Learning (DL) can predict biomarkers directly from digitized cancer histology in a weakly-supervised setting.
From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology
Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer.
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