A curated list of awesome Multi-instance Learning frameworks for Whole Slide Images (WSIs) classification, segmentation, etc.
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Updated
May 27, 2024
A curated list of awesome Multi-instance Learning frameworks for Whole Slide Images (WSIs) classification, segmentation, etc.
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
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.
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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