报告专家：张小群 上海交通大学 教授
报告时间：2023年03月24日14：00—15：30
报告地点：浙江师范大学正阳楼3号楼二楼会议室
报告人简介：
张小群，上海交通大学教授，主要从事数学图像处理，医学图像，数据科学等问题中的数学模型、计算方法与相关数学理论的研究。在应用数学杂志以及交叉学科杂志发表60余篇SCI论文。担任杂志Inverse problems and Imaging, CSIAM Transactions on Applied Mathematics和Applied Mathematics for Modern Challenges编委。 CSIAM大数据与人工智能专业委员会、CSIAM数学与医学交叉学科专业委员会委员，现任教育部科学与工程计算实验室副主任、上海交通大学人工智能研究院数学基础研究中心副主任。
报告概要：
Anomaly detection from medical images is an important task for clinical screening and diagnosis.In generala large dataset ofnormal images is available while only few abnormal images can be collected in clinical practice Bymimicking the diagnosis process ofradiologists,we attempt to tackle this problem by learning a tractable distribution ofnormal images and identify anomalies by differentiating the original image and the reconstructed normal image.More specificallywe propose a normalizingflow based autoencoder for an efficient and tractable representation of normal medical images The anomaly score consists ofthe likelihood originated from the normalizing flow and the reconstruction error of the autoencoderwhich allows to identify the abnormality and provide an interpretability at both image and pixel levels.Experimental evaluation on different medical l images datasets showed that the proposed model outperformed the other approaches by a large margin, which validated the effectiveness and robustness of the proposed method.
