excellent-one

excellent-one's Stars

• jacob-thrasher/TE-SSL

  Code for "TE-SSL: Time and Event-Aware Self Supervised Learning for Alzheimer's Disease Progression Analysis"

  Language:Python4

• thibault-wch/HOPE-for-mild-cognitive-impairment

  [JBHI 2024] This is a code implementation of the hybrid-granularity ordinal learning proposed in the manuscript "HOPE: Hybrid-granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment".

  Language:Python3

• GabrieleLozupone/AXIAL

  This is a code implemention of the diagnosis and XAI framework proposed in the paper "Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans".

  Language:Python333

• ThomasWestfechtel/GSDE

  Gradual Source Domain Expansion for Unsupervised Domain Adaptation

  Language:Python92

• Washington-University/gradunwarp

  Gradient Unwarping in Python

  Language:Python3229

• eeeric-code/I3Net

  I3Net: Inter-Intra-slice Interpolation Network for Medical Slice Synthesis (TMI 2024)

  Language:Python143

• zhaoxin94/awesome-domain-adaptation

  A collection of AWESOME things about domian adaptation

  5.2k879

• iBelieveCJM/Tricks-of-Semi-supervisedDeepLeanring-Pytorch

  PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch

  Language:Python44672

• KaiyangZhou/Dassl.pytorch

  A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.

  Language:Python1.2k172

• xiely-123/SHISRCNet

  Language:Python327

• shap/shap

  A game theoretic approach to explain the output of any machine learning model.

  Language:Jupyter Notebook23.2k3.3k

• easezyc/deep-transfer-learning

  A collection of implementations of deep domain adaptation algorithms

  Language:Python901206

• jindongwang/transferlearning

  Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

  Language:Python13.6k3.8k

• cvenwu/BooksMark

  books mark

  Language:HTML51

• Project-MONAI/MONAI

  AI Toolkit for Healthcare Imaging

  Language:Python6k1.1k

• kenshohara/3D-ResNets-PyTorch

  3D ResNets for Action Recognition (CVPR 2018)

  Language:Python3.9k930

• lwang88/ct_synthesis

  Language:Python12

• seannz/svr

  [CVPR2024] Fully convolutional slice-to-volume reconstruction for single-stack MRI

  Language:Python307

• kimhc6028/relational-networks

  Pytorch implementation of "A simple neural network module for relational reasoning" (Relational Networks)

  Language:Python809160

• vasgaowei/TS-CAM

  Codes for TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization.

  Language:Jupyter Notebook13925

• floodsung/Deep-Learning-Papers-Reading-Roadmap

  Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

  Language:Python38.5k7.3k

• jacobgil/pytorch-grad-cam

  Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

  Language:Python10.9k1.6k

• Dichao-Liu/CMAL

  Language:Python4411

• Fafa-DL/Awesome-Backbones

  Integrate deep learning models for image classification | Backbone learning/comparison/magic modification project

  Language:Python1.6k258

• sidhomj/DeepAPL

  Deep learning for distinguishing morphological features of Acute Promyelocytic Leukemia

  Language:Python103

• JierunChen/FasterNet

  [CVPR 2023] Code for PConv and FasterNet

  Language:Python71857

• cfchen-duke/ProtoPNet

  This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaofan Chen* (Duke University), Oscar Li* (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University) (* denotes equal contribution).

  Language:Python348129

• SrishtiGautam/PRP

  Language:Python42

• Vay-keen/Machine-learning-learning-notes

  周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有所帮助！

  7.4k1.9k

• apple/ml-cvnets

  CVNets: A library for training computer vision networks

  Language:Python1.8k233