Popular GitHub Repositories for Cancer Imaging Research

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

Bayesian Non-Parametric Image Segmentation using HDP-MRF

In this project, we deploy the Bayesian Convolution Neural Networks (BCNN), proposed by Gal and Ghahramani [2015] to classify microscopic images of blood samples (lymphocyte cells). The data contains 260 microscopic images of cancerous and non-cancerous lymphocyte cells. We experiment with different network structures to obtain the model that return lowest error rate in classifying the images. We estimate the uncertainty for the predictions made by the models which in turn can assist a doctor in better decision making. The Stochastic Regularization Technique (SRT), popularly known as Dropout is utilized in the BCNN structure to obtain the Bayesian interpretation.

A machine-learning model that uses a convolutional neural network to classify lung tumors in CT scans, which will help detect lung tumors that might have went unnoticed

DECT-CLUST: DECT image clustering and application to HNSCC tumor segmentation