A curated list of awesome Multi-instance Learning frameworks for Whole Slide Images (WSIs) classification, segmentation, etc.
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Updated
Jun 8, 2024
A curated list of awesome Multi-instance Learning frameworks for Whole Slide Images (WSIs) classification, segmentation, etc.
Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.
In the dynamic landscape of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks like PGD adversarial attack.
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23, MICCAI'24]
WSI classification
Self-Supervised Contrastive Learning for Colon Pathology Classification
Compact Self-Supervised Vision Transformer (cSiT) on Histopathology Images
Scripts for https://www.nature.com/articles/s41598-018-27707-4, using Convolutional Neural Network to detect lung cancer tumor area
MMIR: Multimodal Image Registration
A collection of groovy scripts for digital pathology
The repository has scripts and notebooks to train generative models. We specifically aim to train histo-pathology images which are protected under HIPAA law, to make a robust dataset for future pathology computer vision endeavors.
Lung Preneoplasia Progression via Pathomics
Macenko normalisation of big medical slides
MC2 master's thesis repository.
Whole Slide Digital Pathology Image Tissue Localization
H&E ROI-Level and WSI-Level Nuclei Segmentation with HoVer-Net
Preprocessing module for large histological images
The code for Kernel attention transformer (KAT)
BrainPainter - Brain Visualisation Software
Creating a Crowd sourcing platform to label pathological images. These images are first downloaded from internet using Twitter APIs and then passed through a deep learning model which filter or separate 'Pathological' & 'Non-Pathological' Images.
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