InResponsibleMLbyMateusz Grzyb·Sep 7, 2022Large-scale anonymization of medical imaging data in the DICOM formatA response icon1A response icon1
InResponsibleMLbyAdam Kozłowski·Aug 12, 2022Managing experiment metadata “like a MLOps” — NeptuneOne day comes the great news: the data is available for training. Now, it is the time to start training. We have the goals we want to meet…
InResponsibleMLbyAdrianna Grudzien·Jul 21, 2022LIMEcraft: handcrafted superpixel selection and inspection for Visual eXplanationsThe lack of possibility to interact with explanations makes it difficult to verify and understand exactly how the ML model works. The…
InResponsibleMLbyJakub Wiśniewski·Jun 14, 2022RadioTator: A Tailored Tool for Rapid Medical Text AnnotationCreating an annotation app for text data to use for radiologists.
InResponsibleMLbyPaulina Tomaszewska·May 10, 2022Radiologists’ nightmare — segmentation masksThis blog is the second in our xLungs series about the Responsible Artificial Intelligence for Lung Diseases project. You can check out…
InResponsibleMLbyPaulina Tomaszewska·Apr 14, 2022Towards the largest database of Polish lung medical imagesAI in healthcare is gaining more at more attention. In MI2DataLab, we work on this area in the project: “xLungs — Responsible Artificial…
