Search Results for author:
Found 5 papers, 4 papers with code
B-Cos Aligned Transformers Learn Human-Interpretable Features
Vision Transformers (ViTs) and Swin Transformers (Swin) are currently state-of-the-art in computational pathology.
Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study
Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides.
Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction
Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich contextual information beyond individual entities.
S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning
Evaluations of our framework on two public histopathological datasets show strong improvements in the case of sparse labels: for a H&E-stained colorectal cancer dataset, the accuracy increases by up to 9% compared to supervised cross-entropy loss; for a highly imbalanced dataset of single white blood cells from leukemia patient blood smears, the F1-score increases by up to 6%.
Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations
Alternatively, color augmentation can be applied during training leading to a more robust model without the extra step of color normalization at test time.
