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Found 6 papers, 1 papers with code
Structured Model Pruning for Efficient Inference in Computational Pathology
In this work, we demonstrate that model pruning, as a model compression technique, can effectively reduce inference cost for computational and digital pathology based analysis with a negligible loss of analysis performance.
Monitoring Shortcut Learning using Mutual Information
The failure of deep neural networks to generalize to out-of-distribution data is a well-known problem and raises concerns about the deployment of trained networks in safety-critical domains such as healthcare, finance and autonomous vehicles.
Domain-Agnostic Clustering with Self-Distillation
Recent advancements in self-supervised learning have reduced the gap between supervised and unsupervised representation learning.
Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images
The feature extractor model is fine-tuned using hierarchical target labels of WSIs, i. e., anatomic site and primary diagnosis.
Representation Learning of Histopathology Images using Graph Neural Networks
Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology.
Learning Permutation Invariant Representations using Memory Networks
Many real-world tasks such as classification of digital histopathology images and 3D object detection involve learning from a set of instances.
