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Found 9 papers, 2 papers with code
ML-Dev-Bench: Comparative Analysis of AI Agents on ML development workflows
In this report, we present ML-Dev-Bench, a benchmark aimed at testing agentic capabilities on applied Machine Learning development tasks.
Learning biologically relevant features in a pathology foundation model using sparse autoencoders
Previous works on interpretability for machine learning models on pathology images have revolved around methods such as attention value visualization and deriving human-interpretable features from model heatmaps.
PLUTO: Pathology-Universal Transformer
Pathology is the study of microscopic inspection of tissue, and a pathology diagnosis is often the medical gold standard to diagnose disease.
ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology
Secondly, dependency on domain labels prevents the use of pathology images without domain labels to improve model performance.
Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology
Machine learning algorithms have the potential to improve patient outcomes in digital pathology.
SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology
We leverage the idea that decoupling feature and classifier learning can lead to improved decision boundaries for label imbalanced datasets.
Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts
Histopathology images are gigapixel-sized and include features and information at different resolutions.
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society.
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks
% We observe that GNNs can identify P0 close to the theoretical bound on accuracy, without explicit input of dynamics or its parameters.
