Popular GitHub Repositories for Pancreatic Cancer

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

BCDU-Net : Medical Image Segmentation

天池医疗AI大赛[第一季]：肺部结节智能诊断 UNet/VGG/Inception/ResNet/DenseNet

BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk

Training and evaluating a variational autoencoder for pan-cancer gene expression data

A Machine Learning and Deep Learning based webapp used to predict multiple diseases.

[Nature Machine Intelligence 2024] Code and evaluation repository for the paper

BioDynaMo is a high-performance and modular, agent-based simulation platform.

Corresponding code of 'Quiros A.C., Murray-Smith R., Yuan K. Pathology GAN: Learning deep representations of cancer tissue. Proceedings of The 3rd International Conference on Medical Imaging with Deep Learning (MIDL) 2020'

Contextual Attention Network: Transformer Meets U-Net

Federated Learning based Deep Learning. Docs: https://fets-ai.github.io/Front-End/

Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.

TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation

This is the official repository for our CVPR 2023 paper 'Task-Specific Fine-Tuning via Variational Information Bottleneck for Weakly-Supervised Pathology Whole Slide Image Classification'.

Diseases Detection from NIH Chest X-ray data

Official release of MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer https://ieeexplore.ieee.org/document/10006398

Homomorphic Encryption and Federated Learning based Privacy-Preserving

Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification

Corresponding code of 'Quiros A.C.+, Coudray N.+, Yeaton A., Yang X., Chiriboga L., Karimkhan A., Narula N., Pass H., Moreira A.L., Le Quesne J.*, Tsirigos A.*, and Yuan K.* Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides. 2024'