A review of content-based image retrieval systems in medical applications - clinical benefits and future directions

By Henning Müller, Nicolas Michoux, David Bandon, Antoine Geissbühler. Int. J. Medical Informatics, 2004

503 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/0e578f3884e60b224ac65b3939942a927561feba

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Image retrieval: Ideas, influences, and trends of the new age (2008, methodology): “feature weights based on user logs has been explored in [Muller et al. 2004].”
A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations (2017, background): “The goal of M-CBIR is to assist radiologists in the clinical decisionmaking process by finding clinical cases in large archives thatmost resemble a given case [10,11].”
Content-based image retrieval for brain MRI: An image-searching engine and population-based analysis to utilize past clinical data for future diagnosis (2015, methodology): “…technology for direct image searching, called content-based image retrieval (CBIR), is similar to that involved in face recognition (for review, see Müller, 2004), and is highly anticipated advancement in medical imaging for CT and MRI (Greenspan and Pinhas, 2007; Rahman et al., 2007; Robinson,…”
Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making (2019, methodology): “In medicine, CBIR has been used to help experts make better decisions, by identifying visually similar images for a given query image (1, 23, 34, 35, 42).”
Content-based image retrieval: approaches and trends of the new age (2005, unknown): “Surveys also exist on closely related topics such as relevance feedback [119], highdimensional indexing of multimedia data [9], applications of content-based image retrieval to medicine [74], and applications to art and cultural imaging [15].”
A Decade Survey of Content Based Image Retrieval Using Deep Learning (2020, unknown): “[2] in 2000, [20] in 2002, [21] in 2004, [22] in 2006, [23] in 2007, [24] in 2008, [25] in 2014 and [26] in 2017.”
Machine Learning and Radiology (2012, unknown): “For example, in the Radiology Department of the University Hospital of Geneva, 12,000 images were produced daily in 2002 (Muller et al., 2004).”
Features for image retrieval: an experimental comparison (2008, background): “For example, medical doctors have to access large amounts of images daily [1], home-users often have image databases of thousands of images [2], and journalists also need to search for images by various criteria [3, 4].”