ZeroCostDL4Mic: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy

Official Repository of NeurIPS 2023 - MedFM Challenge

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…

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 rela…

Code for "Segment Anything Model for Medical Image Analysis: an Experimental Study" in Medical Image Analysis

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Tool for robust segmentation of >100 important anatomical structures in CT and MR images

AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

MedLSAM: Localize and Segment Anything Model for 3D Medical Images

A nnU-Netv2 based acceleration solution for Abdominal organs and tumor segmentation.

The easiest tool for experimenting with radiomics features.

Curated list of Python resources for data science.

MONAI Versatile Imaging Segmentation and Annotation

A Slicer extension to provide a GUI around pyradiomics

Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics

Public repo for DeepLearning.AI MLEP Specialization

scikit-learn-mooc

Techniques for deep learning with satellite & aerial imagery

Brainchop: In-browser 3D MRI rendering and segmentation

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

VIP cheatsheets for Stanford's CS 230 Deep Learning

VIP cheatsheets for Stanford's CS 229 Machine Learning

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

A list of Medical imaging datasets.