MIC-DKFZ/medicaldetectiontoolkit
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
This toolkit helps medical researchers and practitioners automatically identify and localize anatomical structures or lesions within medical images (like CT or MRI scans). You input raw 2D or 3D medical images, potentially with existing bounding box or pixel-level annotations, and it outputs precise detections of objects of interest. This is designed for medical imaging scientists, radiologists, or clinicians who need to develop or apply automated detection models.
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Use this if you are working with 2D or 3D medical images and need a flexible framework to train, evaluate, and deploy object detection models to pinpoint specific features or anomalies.
Not ideal if you are looking for a plug-and-play solution without any programming or deep learning expertise, as this requires setup and customization for your specific datasets.
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Language
Python
License
Apache-2.0
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Last pushed
Jun 17, 2024
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