Learning Interclass Relations for Intravenous Contrast Phase Classification in CT

Raouf Muhamedrahimov, Amir Bar, Ayelet Akselrod-Ballin

Research output: Contribution to journalConference articlepeer-review


In classification, categories are typically treated as independent of one-another. In many problems, however, this neglects the natural relations that exist between categories, which are often dictated by an underlying biological or physical process. In this work, we propose novel formulations of the classification problem, aimed at reintroducing class relations into the training process. We demonstrate the benefit of these approaches for the classification of intravenous contrast enhancement phase in CT images, an important task in the medical imaging domain. First, we propose manual ways reintroduce knowledge about problem-specific interclass relations into the training process. Second, we propose a general approach to jointly learn categorical label representations that can implicitly encode natural interclass relations, alleviating the need for strong prior assumptions or knowledge. We show that these improvements are most significant for smaller training sets, typical in the medical imaging domain where access to large amounts of labelled data is often not trivial.

Original languageEnglish
Pages (from-to)507-519
Number of pages13
JournalProceedings of Machine Learning Research
StatePublished - 2021
Event4th Conference on Medical Imaging with Deep Learning, MIDL 2021 - Virtual, Online, Germany
Duration: 7 Jul 20219 Jul 2021


Dive into the research topics of 'Learning Interclass Relations for Intravenous Contrast Phase Classification in CT'. Together they form a unique fingerprint.

Cite this