yanfang-research Goto Github PK

mediar-napari

medical-ai-research-foundations

medical-sam-adapter

Adapting Segment Anything Model for Medical Image Segmentation

medicalnet

Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project provides a series of 3D-ResNet pre-trained models and relative code.

medsam

This the official repository for MedSAM: Segment Anything in Medical Images.

mi-aug

ECG data augmentation for MI detection and culprit vessel localization.

mibc-predictive-models

This repository includes generated data and codes for paper: 'Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer based on nuclear morphology and tissue architecture'.'

microdeconvolution

Color deconvolution and stain segmentation for immunohistochemical (IHC) tumor sections

mil-nature-medicine-2019

mil_histology

This repository contains open code for weakly supervised histology whole slide image classification. Also, it containts link to SICAP-MIL dataset, a public available dataset of prostate whole slide images.

mixed_supervision

MICCAI2022: Multiple Instance Learning with Mixed Supervision in Gleason Grading.

mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

modelsgenesis

Official Keras & PyTorch Implementation and Pre-trained Models for Models Genesis - MICCAI 2019

moma

MOMA

monailabel

MONAI Label is an intelligent open source image labeling and learning tool.

ms-da-mil-cnn

Multi-scale Domain-adversarial Multiple Instance Learning CNN (CVPR2020)

ms-da-mil-cnn-1

PyTorch implementation of MS-DA-MIL

multiple_instance_learning_instance_based

nmi-wsi-diagnosis

Pathologist-level interpretable whole-slide cancer diagnosis with deep learning

nnunet

panda-prostate-cancer-grade-assessment

path_r_cnn

Path R-CNN: Object Detection and Segmentation on Pathology Image.

pathology

pathology-artifact-detection

Quality control of whole-slide images through multi-class semantic segmentation of artifacts

pathology-whole-slide-data

A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.

pd-l1_annotator

pd-l1_predictor

pdl1_sp142_breast_ic_score

playground

A central hub for gathering and showcasing amazing projects that extend OpenMMLab with SAM and other exciting features.

plip

Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI. PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.