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

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

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

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

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

AI-based pathology predicts origins for cancers of unknown primary - Nature

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

The MAMA-MIA Dataset: A Multi-Center Breast Cancer DCE-MRI Public Dataset with Expert Segmentations

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

World's fastest DCE MRI analysis toolkit

Probabilistic topic model for identifying cellular micro-environments.

Open source of Pyradiomics extension

PySERA – Open-Source Standardized Python Library for Automated, Scalable, and Reproducible Handcrafted and Deep Radiomics

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.

The AstroPath Pipeline was developed to process whole slide multiplex immunofluorescence data from microscope to database at single cell resolution.

Code accompanying our ICVGIP 2016 paper

Implementation of PySERA in 3D Slicer

Official repository for Characterization of tumor heterogeneity through segmentation-free representation learning on multiplexed imaging data

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

A production-grade deep learning system for automated skin lesion classification using the HAM10000 dataset. This system provides training, evaluation, and real-time inference capabilities for detecting seven types of skin lesions.

Deep ConvNets based eye cancer detection

Predict survival time from PET scans

Python implementation of topology descriptors which capture subtle sharpness and curvature differences along the surface of diseased pathologies on imaging.

Bayesian Non-Parametric Image Segmentation using HDP-MRF

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

Skin cancer classification using transfer learning

Reference MATLAB and Python implementations of the RADISTAT algorithm

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

No description provided.

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.