58 skills found · Page 1 of 2
OHIF / ViewersOHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages
AIM-Harvard / PyradiomicsOpen-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
lishen / End2end All ConvDeep Learning to Improve Breast Cancer Detection on Screening Mammography
QIICR / Dcmqidcmqi (DICOM for Quantitative Imaging) is a C++ library for conversion between imaging research formats and the standard DICOM representation for image analysis results
CBICA / CaPTkCancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk
mahmoodlab / TOADAI-based pathology predicts origins for cancers of unknown primary - Nature
AIM-Harvard / Foundation Cancer Image Biomarker[Nature Machine Intelligence 2024] Code and evaluation repository for the paper
LidiaGarrucho / MAMA MIAThe MAMA-MIA Dataset: A Multi-Center Breast Cancer DCE-MRI Public Dataset with Expert Segmentations
hugofigueiras / Breast Cancer Imaging DatasetsCentralized resource for breast imaging and histopathology datasets (Ultrasound, DBT, Mammography, MRI). Includes use cases, characteristics, and access info—supporting development of diagnostic tools, training algorithms, and advancing breast cancer research.
nadeemlab / CIRClinically-Interpretable Radiomics [MICCAI'22, CMPB'21]
davidssmith / DCEMRI.jlWorld's fastest DCE MRI analysis toolkit
crowds-cure / CancerCrowd-sourcing annotations of medical images to advance cancer research
NIH-MIP / Radiology Image Preprocessing For Deep LearningA Quick Guide on Radiology Image Pre-processing for Deep Learning Applications in Prostate Cancer Research
calico / Spatial LdaProbabilistic topic model for identifying cellular micro-environments.
mistersharmaa / BreastCancerPredictionBreast cancer has the second highest mortality rate in women next to lung cancer. As per clinical statistics, 1 in every 8 women is diagnosed with breast cancer in their lifetime. However, periodic clinical check-ups and self-tests help in early detection and thereby significantly increase the chances of survival. Invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, there arises the need for a more robust, fast, accurate, and efficient non-invasive cancer detection system. Early detection can give patients more treatment options. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. Then, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. AI will become a transformational force in healthcare and soon, computer vision models will be able to get a higher accuracy when researchers have the access to more medical imaging datasets. The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective. We have developed a computer vision model to detect breast cancer in histopathological images. Two classes will be used in this project: Benign and Malignant
SlicerProstate / SlicerProstateAn extension to 3D Slicer to support quantitative imaging and image-guided interventions research in prostate cancer.
zhenweishi / Py RexOpen source of Pyradiomics extension
radiuma-com / PySERAPySERA – Open-Source Standardized Python Library for Automated, Scalable, and Reproducible Handcrafted and Deep Radiomics
radxtools / CollageradiomicsPython 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.
AstroPathJHU / AstroPathPipelineThe AstroPath Pipeline was developed to process whole slide multiplex immunofluorescence data from microscope to database at single cell resolution.
