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In scoring systems used to measure the endoscopic activity of ulcerativecolitis, such as Mayo endoscopic score or Ulcerative Colitis Endoscopic IndexSeverity, levels increase with severity of the disease activity. Such relativeranking among the scores makes it an ordinal regression problem. On the otherhand, most studies use categorical cross-entropy loss function to train deeplearning models, which is not optimal for the ordinal regression problem. Inthis study, we propose a novel loss function, class distance weightedcross-entropy (CDW-CE), that respects the order of the classes and takes thedistance of the classes into account in calculation of the cost. Experimentalevaluations show that models trained with CDW-CE outperform the models trainedwith conventional categorical cross-entropy and other commonly used lossfunctions which are designed for the ordinal regression problems. In addition,the class activation maps of models trained with CDW-CE loss are moreclass-discriminative and they are found to be more reasonable by the domainexperts.

Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation