camelyon16

Open source tools for computational pathology - Nature BME

Cancer metastasis detection with neural conditional random field (NCRF)

Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification (ECCV2024)

Official code for "Self-Supervised driven Consistency Training for Annotation Efficient Histopathology Image Analysis" Published in Medical Image Analysis (MedIA) Journal, Oct, 2021.

PFA-ScanNet: Pyramidal Feature Aggregation With Synergistic Learning for Breast Cancer Metastasis Analysis (Architecture Only Pytorch Implementation).

AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification

The implementation of our pricai18 paper "Multiple Visual Fields Cascaded Convolutional Neural Network for Breast Cancer Detection."

Image Processing and CV on Whole Slide Images

Attention-based Deep Multiple Instance Learning for Whole-Slide Image Dataset

This repository contains several popular Attention-based Multiple Instance Learning (ABMIL) algorithms in PyTorch implementation.

IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology

Dissertation completed for the award of MSci in Computer Science. This dissertation is about automated breast cancer detection in low-resolution whole-slide pathology images using a deep convolutional neural network pipeline.

Computationally efficient multi-scale foundation model for computational pathology. 71M parameters, runs end-to-end on a single consumer GPU.

Thumbnail-level tissue segmentation on CAMELYON16 with ALBS, a preprocessing-based masker, and ALBSRefiner, a compact learned refiner.

Deep Learning for Whole Slide Image Analysis in Digital Pathology — CLAM, TransMIL, and attention-based MIL for cancer detection on Camelyon16

A project proposal with currently undergoing prelim. experiments with image classification tasks performed with CNN and ViT models

Statistical analysis of compression sensitivity in tumor vs. normal WSI patches

PPO-based reinforcement learning for whole-slide cancer image navigation (CAMELYON16) with reward shaping, strict evaluation, and foundation-model embeddings.