Search Results for author:
Found 3 papers, 1 papers with code
SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology
Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides.
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging.
Unsupervised Histopathology Image Synthesis
We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with on-the-fly generated adversarial examples.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
