OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages

Detecting various characteristics of glioblastoma using Deep Learning

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

Empowering healthcare with AI, LeukoVision automates the detection of leukemia cells in blood smear images. Revolutionizing leukemia care with speed and accuracy.

PIXI is an XNAT plugin designed to help manage and analyze preclinical imaging data.

dcmqi (DICOM for Quantitative Imaging) is a free, open source C++ library for conversion between imaging research formats and the standard DICOM representation for image analysis results

Python Open-source package for medical images processing and radiomics features extraction.

Python Open-source package for medical images processing and radiomics features extraction.

[IJHCS] An assistant prototype for breast cancer diagnosis prepared with a multimodality strategy. The work was published in the International Journal of Human-Computer Studies.

ImaGene: A multi-omic ML/AI software with guided operational reports and supporting files

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

SOPHYSM - SOlid tumors PHYlogenetic Spatial Modeller - Julia GUI for Histological Analysis and Cancer Simulation

Read ImmunoHistoChimic images, segmented cells (pre-trained stardist model) and classifyed

Python Implementation of the CoLlAGe radiomics descriptor. CoLlAGe captures subtle anisotropic differences in disease pathologies by measuring entropy of co-occurrences of voxel-level gradient orientations on imaging computed within a local neighborhood.

This project aims to compare the performance of popular deep learning models, Convolutional Neural Network (CNN) and Xception with their added architectural modifications, for image classification on the Ham10000 dataset. The Ham10000 dataset contains 10,015 dermatoscopic images of pigmented skin lesions, which are categorized into seven different

Probabilistic topic model for identifying cellular micro-environments.

Project focuses on diagnosing cancer through image analysis. It utilizes machine learning models and techniques to analyze medical images and classify cancerous cells or tumors. It aims to improve cancer diagnosis accuracy and assist healthcare professionals.

Clinically-Interpretable Radiomics [MICCAI'22, CMPB'21]

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