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Anomaly Detection for Medical Images Using Heterogeneous Auto-Encoder

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机构: [1]Beijing Inst Technol, Beijing 100081, Peoples R China [2]Univ Liverpool, Dept Eye & Vis Sci, Liverpool L7 8TX, England [3]Capital Med Univ, Beijing Tongren Hosp, Beijing Inst Ophthalmol, Beijing 100054, Peoples R China [4]Henan Prov Acad Med Sci, Zhengzhou 450046, Peoples R China
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关键词: Anomaly detection Magnetic resonance imaging auto-encoder heterogeneous network

摘要:
Anomaly detection is an important task for medical image analysis, which can alleviate the reliance of supervised methods on large labelled datasets. Most existing methods use a pixel-wise self-reconstruction framework for anomaly detection. However, there are two challenges of these studies: 1) they tend to overfit learning an identity mapping between the input and output, which leads to failure in detecting abnormal samples; 2) the reconstruction considers the pixel-wise differences which may lead to an undesirable result. To mitigate the above problems, we propose a novel heterogeneous Auto-Encoder (Hetero-AE) for medical anomaly detection. Our model utilizes a convolutional neural network (CNN) as the encoder and a hybrid CNN-Transformer network as the decoder. The heterogeneous structure enables the model to learn the intrinsic information of normal data and enlarge the difference on abnormal samples. To fully exploit the effectiveness of Transformer in the hybrid network, a multi-scale sparse Transformer block is proposed to trade off modelling long-range feature dependencies and high computational costs. Moreover, the multi-stage feature comparison is introduced to reduce the noise of pixel-wise comparison. Extensive experiments on four public datasets (i.e., retinal OCT, chest X-ray, brain MRI, and COVID-19) verify the effectiveness of our method on different imaging modalities for anomaly detection. Additionally, our method can accurately detect tumors in brain MRI and lesions in retinal OCT with interpretable heatmaps to locate lesion areas, assisting clinicians in diagnosing abnormalities efficiently.

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出版当年[2023]版:
大类 | 1 区 计算机科学
小类 | 1 区 计算机:人工智能 1 区 工程:电子与电气
最新[2025]版:
大类 | 1 区 计算机科学
小类 | 1 区 计算机:人工智能 1 区 工程:电子与电气
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出版当年[2022]版:
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
最新[2024]版:
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

影响因子: 最新[2024版] 最新五年平均 出版当年[2022版] 出版当年五年平均 出版前一年[2021版] 出版后一年[2023版]

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第一作者机构: [1]Beijing Inst Technol, Beijing 100081, Peoples R China
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通讯机构: [1]Beijing Inst Technol, Beijing 100081, Peoples R China [2]Univ Liverpool, Dept Eye & Vis Sci, Liverpool L7 8TX, England
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