pylinac is a Python library for performing TG-142 QA tasks on linear accelerators. It provides high-level analysis modules for common radiotherapy QA tests—VMAT, Winston-Lutz, PIPS, field analysis, planar imaging, log analysis, CT, and star shot—plus lower-level core modules for image handling, gamma analysis, ROI calculations, and structured results. The library is used as a dependency in clinical QA workflows and exposes a consistent API: instantiate an analysis class with a DICOM/log file path, call `.analyze(...)`, then access `.results_data()`, `.publish_pdf()`, or `.plot_analyzed_image()`.
This repository provides the serving infrastructure and fine-tuning examples for MedSigLIP, a medical vision-language model (SigLIP variant) that encodes medical images and text into a shared embedding space. The codebase is dominated by a production-grade serving system: a Triton Inference Server backend wrapped in a Gunicorn application, deployed on Google Cloud Vertex AI. It includes modular data accessors for DICOM (whole-slide and generic), GCS, HTTP images, and inline bytes, with modality-specific preprocessing (ICC profile correction, windowing, dynamic range rescaling). The serving layer handles batch prediction, authentication, structured logging, and health checks. While notebooks demonstrate fine-tuning and data-efficient classifier training using Hugging Face, the core of the repository is the serving stack, making it primarily an application with strong data-research hybridity. The code is well-structured with clear abstractions (abstract data accessors, model runner, predictor), thorough unit tests, and reusable patterns like three-tier ICC profile caching and thread-safe logging. However, it is tightly coupled to Google Cloud and medical imaging standards, and some complexity arises from forking and dependency management.