medical-image-analysis

Medical imaging processing for AI applications.

[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

A collection of resources on applications of Transformers in Medical Imaging.

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

⚡High Performance DICOM Medical Image Parser in Go.

Automated lung segmentation in CT

[MICCAI 2019 Young Scientist Award] [MedIA Best Paper Award] Models Genesis: self-supervised pre-training for 3D medical images. Learns transferable representations from unlabeled CT and MRI volumes, then fine-tunes for downstream segmentation and classification. Keras and PyTorch weights included.

Computational Pathology Toolbox developed by TIA Centre, University of Warwick.

[MICCAI 2023] MedNeXt is a fully ConvNeXt architecture for 3D medical image segmentation.

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.