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Updated: Jul 2, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
The ImageCLEFmed medical image retrieval task test collection
William Hersh1, Henning Müller, Jayashree Kalpathy-Cramer
1Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd., BICC, Portland, OR 97239, USA. hersh@ohsu.edu
Abstract:
A growing number of clinicians, educators, researchers, and others use digital images in their work and search for them via image retrieval systems. Yet, this area of information retrieval is much less understood and developed than searching for text-based content, such as biomedical literature and its derivations. The goal of the ImageCLEF medical image retrieval task (ImageCLEFmed) is to improve understanding and system capability in search for medical images. In this paper, we describe the development and use of a medical image test collection designed to facilitate research with image retrieval systems and their users. We also provide baseline results with the new collection and describe them in the context of past research with portions of the collection.
