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attention_flow

blrm

Pytorch implementation of Bayesian Statistics Guided Label Refurbishment Mechanism

cncl-denoising

Pytorch implementation of Content-Noise Complementary Learning for Medical Image Denoising.

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

deep-meta-metric-learning

fundusdata-fs

labelimg

LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.

libfewshot

LibFewShot: A Comprehensive Library for Few-shot Learning.

medfmc_fewshot_baseline

Implementation of few-shot baseline for MedFMC

meta-transfer-learning

TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)

mmdetection

OpenMMLab Detection Toolbox and Benchmark

pytorch-model-compare

Compare neural networks by their feature similarity

qasystemonmedicalkg

A tutorial and implement of disease centered Medical knowledge graph and qa system based on it。知识图谱构建，自动问答，基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱，并以该知识图谱完成自动问答与分析服务。

quantus

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

retfound_mae

RETFound - A foundation model for retinal image

sam-adapter-pytorch

Adapting Meta AI's Segment Anything to Downstream Tasks with Adapters and Prompts

segment-geospatial

A Python package for segmenting geospatial data with the Segment Anything Model (SAM)

transformer-explainability

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

transformer-mm-explainability

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

visualize-transformer-resnet18