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Found 5 papers, 1 papers with code
Long-MIL: Scaling Long Contextual Multiple Instance Learning for Histopathology Whole Slide Image Analysis
Histopathology image analysis is the golden standard of clinical diagnosis for Cancers.
Exploring Unsupervised Cell Recognition with Prior Self-activation Maps
The gradient information in the shallow layers of the network is aggregated to generate prior self-activation maps.
Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images
To this end, we propose a self-supervised learning based approach with a Prior Self-activation Module (PSM) that generates self-activation maps from the input images to avoid labeling costs and further produce pseudo masks for the downstream task.
Weakly Supervised Learning for cell recognition in immunohistochemical cytoplasm staining images
We have evaluated our framework on immunohistochemical cytoplasm staining images, and the results demonstrate that our method outperforms recent cell recognition approaches.
Generalizing Nucleus Recognition Model in Multi-source Images via Pruning
In addition, the model is optimized by fine-tuning on merged domains to eliminate the interference of class mismatching among various domains.
