Medical imaging software

Viewers is a zero-footprint DICOMweb medical imaging viewer and a modular plugin framework. It serves as a diagnostic interface for rendering 2D and 3D medical images, providing a web-based clinical workflow engine to automate image layouts and toolsets. The project distinguishes itself through a highly extensible architecture that allows for the development of custom clinical workflows, specialized viewing modes, and the integration of external functional extensions. It includes a dedicated command line interface for managing these plugins and supports white-labeling through a comprehensive

Weasis is a professional medical imaging platform designed for the visualization, analysis, and integration of clinical studies. It functions as a modular framework that supports the rendering of diverse medical data, including multi-modal images, 3D volumes, segmentations, and structured reports. By utilizing standardized protocols, the platform serves as a comprehensive viewer that connects directly to hospital archives and clinical information systems. The platform distinguishes itself through a highly extensible, plugin-based architecture that allows developers to integrate custom tools,

TotalSegmentator is a medical image segmentation tool and AI-driven organ segmenter designed to isolate anatomical structures from CT scans. It functions as a deep learning anatomy parser and quantitative radiomics analyzer, providing a framework for identifying diverse body tissues and bones to create precise anatomical masks. The system distinguishes itself through a comprehensive medical analysis suite that includes patient biometric estimation for demographics such as age, sex, weight, and height. It further provides specialized clinical index calculations and modality and phase classific

ECCVW 2022 The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

Official Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" - MICCAI 2021

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers, AIR 2023.

by Boxiang Yun, Baiying Lei, Jieneng Chen, Huiyu Wang, Song Qiu, Wei Shen, Qingli Li, Yan Wang*

This is an image segmentation framework and masking toolkit for constructing binary and multi-class neural network architectures. It serves as a deep learning encoder wrapper that integrates pre-trained convolutional neural network architectures into semantic segmentation models. The library enables the use of pre-trained backbones to isolate complex patterns and leverages transfer learning to accelerate training. It provides a collection of overlap-based loss functions and precision metrics specifically designed to evaluate and refine the accuracy of image masks. The toolkit covers the full

This project is a collection of specialized toolkits and an agent skill library designed to equip large language model agents with the capabilities to perform complex scientific research across biology, chemistry, medicine, and physics. It provides a structured framework of integration paths and tools that allow agents to execute multi-step research workflows. The system is distinguished by its domain-specific toolsets, including a bioinformatics toolkit for genomic and single-cell analysis, a cheminformatics toolset for drug-target binding and lead compound optimization, and a multi-omics an

This project provides a deep learning architecture designed to identify and isolate distinct objects within images by generating precise pixel-level masks. It functions as a browser-based inference engine, enabling the execution of complex machine learning models directly within web environments without requiring server-side processing. The system distinguishes itself by utilizing hardware-accelerated execution and parallel processing to achieve real-time segmentation speeds. It supports prompt-based mask decoding, allowing users to generate spatial masks by providing specific points or boxes

mimic-code is a clinical data analysis framework and toolset for processing deidentified electronic health records and intensive care unit data. It provides a healthcare SQL query library and a processing tool to transform raw health records into formats suitable for longitudinal analysis and machine learning. The project features a medical research notebook environment that integrates with cloud-hosted datasets, allowing for remote querying and analysis. It includes a DICOM imaging pipeline to retrieve chest radiographs and link medical imaging with structured clinical metadata. The framewo