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Found 8 papers, 5 papers with code
Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology
We believe, that our work can serve as a proof-of-concept for more advanced LLM-agents in the medical domain.
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.
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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A Good Feature Extractor Is All You Need for Weakly Supervised Pathology Slide Classification
We question this belief in the context of weakly supervised whole slide image classification, motivated by the emergence of powerful feature extractors trained using self-supervised learning on diverse pathology datasets.
Deep Multiple Instance Learning with Distance-Aware Self-Attention
We evaluate our model on a custom MNIST-based MIL dataset that requires the consideration of relative spatial information, as well as on CAMELYON16, a publicly available cancer metastasis detection dataset, where we achieve a test AUROC score of 0. 91.
HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks
The presence and density of specific types of immune cells are important to understand a patient's immune response to cancer.
Determining Chess Game State From an Image
Identifying the configuration of chess pieces from an image of a chessboard is a problem in computer vision that has not yet been solved accurately.
