3 papers
cs.CV2025
Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology
Nirhoshan Sivaroopan, Chamuditha Jayanga Galappaththige, Chalani Ekanayake +4
Machine-learning-assisted cancer subtyping is a promising avenue in digital pathology. Cancer subtyping models, however, require careful training using expert annotations so that t…
cs.CV2024
Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning
Hasindri Watawana, Kanchana Ranasinghe, Tariq Mahmood +3
Self-supervised representation learning has been highly promising for histopathology image analysis with numerous approaches leveraging their patient-slide-patch hierarchy to learn…
cs.CV2023
Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology
Nirhoshan Sivaroopan, Chamuditha Jayanga, Chalani Ekanayake +6
Deep neural network models can learn clinically relevant features from millions of histopathology images. However generating high-quality annotations to train such models for each…