Margin-Aware Intra-Class Novelty Identification for Medical Images

• URL: http://arxiv.org/abs/2108.00117v1
• Date: Sat, 31 Jul 2021 00:10:26 GMT
• Title: Margin-Aware Intra-Class Novelty Identification for Medical Images
• Authors: Xiaoyuan Guo, Judy Wawira Gichoya, Saptarshi Purkayastha and Imon Banerjee
• Abstract summary: We propose a hybrid model - Transformation-based Embedding learning for Novelty Detection (TEND) With a pre-trained autoencoder as image feature extractor, TEND learns to discriminate the feature embeddings of in-distribution data from the transformed counterparts as fake out-of-distribution inputs.
• Score: 2.647674705784439
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Traditional anomaly detection methods focus on detecting inter-class variations while medical image novelty identification is inherently an intra-class detection problem. For example, a machine learning model trained with normal chest X-ray and common lung abnormalities, is expected to discover and flag idiopathic pulmonary fibrosis which a rare lung disease and unseen by the model during training. The nuances from intra-class variations and lack of relevant training data in medical image analysis pose great challenges for existing anomaly detection methods. To tackle the challenges, we propose a hybrid model - Transformation-based Embedding learning for Novelty Detection (TEND) which without any out-of-distribution training data, performs novelty identification by combining both autoencoder-based and classifier-based method. With a pre-trained autoencoder as image feature extractor, TEND learns to discriminate the feature embeddings of in-distribution data from the transformed counterparts as fake out-of-distribution inputs. To enhance the separation, a distance objective is optimized to enforce a margin between the two classes. Extensive experimental results on both natural image datasets and medical image datasets are presented and our method out-performs state-of-the-art approaches.

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