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Luo Y, Ma Y, Yang Z. Multi-resolution auto-encoder for anomaly detection of retinal imaging. Phys Eng Sci Med 2024; 47:517-529. [PMID: 38285270 DOI: 10.1007/s13246-023-01381-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/27/2023] [Indexed: 01/30/2024]
Abstract
Identifying unknown types of diseases is a crucial step in preceding retinal imaging classification for the sake of safety, which is known as anomaly detection of retinal imaging. However, the widely-used supervised learning algorithms are not suitable for this problem, since the data of the unknown category is unobtainable. Moreover, for retinal imaging with different types of anomalous regions, using a single-resolution input causes information loss. Therefore, we propose an unsupervised auto-encoder model with multi-resolution inputs and outputs. We provide a theoretical understanding of the effectiveness of reconstruction error and the improvement of self-supervised learning for anomaly detection. Our experiments on two widely-used retinal imaging datasets show that the proposed methods are superior to other methods, and further experiments verify the validity of each part of the proposed method.
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Affiliation(s)
- Yixin Luo
- School of Mathematical Sciences, University of Science and Technology of China, No. 96 Jinzhai Road, Hefei, 230026, Anhui, China
| | - Yangling Ma
- School of Mathematical Sciences, Suzhou University of Science and Technology, No. 99 Xuefu Road, Suzhou, 215009, Jiangsu, China
| | - Zhouwang Yang
- School of Mathematical Sciences, University of Science and Technology of China, No. 96 Jinzhai Road, Hefei, 230026, Anhui, China.
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2
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Lu S, Zhang W, Guo J, Liu H, Li H, Wang N. PatchCL-AE: Anomaly detection for medical images using patch-wise contrastive learning-based auto-encoder. Comput Med Imaging Graph 2024; 114:102366. [PMID: 38471329 DOI: 10.1016/j.compmedimag.2024.102366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 03/04/2024] [Accepted: 03/04/2024] [Indexed: 03/14/2024]
Abstract
Anomaly detection is an important yet challenging task in medical image analysis. Most anomaly detection methods are based on reconstruction, but the performance of reconstruction-based methods is limited due to over-reliance on pixel-level losses. To address the limitation, we propose a patch-wise contrastive learning-based auto-encoder for medical anomaly detection. The key contribution is the patch-wise contrastive learning loss that provides supervision on local semantics to enforce semantic consistency between corresponding input-output patches. Contrastive learning pulls corresponding patch pairs closer while pushing non-corresponding ones apart between input and output, enabling the model to learn local normal features better and improve discriminability on anomalous regions. Additionally, we design an anomaly score based on local semantic discrepancies to pinpoint abnormalities by comparing feature difference rather than pixel variations. Extensive experiments on three public datasets (i.e., brain MRI, retinal OCT, and chest X-ray) achieve state-of-the-art performance, with our method achieving over 99% AUC on retinal and brain images. Both the contrastive patch-wise supervision and patch-discrepancy score provide targeted advancements to overcome the weaknesses in existing approaches.
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Affiliation(s)
- Shuai Lu
- Beijing Institute of Technology, Beijing, 100081, China
| | - Weihang Zhang
- Beijing Institute of Technology, Beijing, 100081, China
| | - Jia Guo
- Beijing Institute of Technology, Beijing, 100081, China
| | - Hanruo Liu
- Beijing Institute of Technology, Beijing, 100081, China; Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, 100005, China.
| | - Huiqi Li
- Beijing Institute of Technology, Beijing, 100081, China.
| | - Ningli Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, 100005, China
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Chen Z, Yao L, Liu Y, Han X, Gong Z, Luo J, Zhao J, Fang G. Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation. Sci Rep 2024; 14:9784. [PMID: 38684904 PMCID: PMC11059262 DOI: 10.1038/s41598-024-60668-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 04/25/2024] [Indexed: 05/02/2024] Open
Abstract
Accurate multi-organ segmentation in 3D CT images is imperative for enhancing computer-aided diagnosis and radiotherapy planning. However, current deep learning-based methods for 3D multi-organ segmentation face challenges such as the need for labor-intensive manual pixel-level annotations and high hardware resource demands, especially regarding GPU resources. To address these issues, we propose a 3D proxy-bridged region-growing framework specifically designed for the segmentation of the liver and spleen. Specifically, a key slice is selected from each 3D volume according to the corresponding intensity histogram. Subsequently, a deep learning model is employed to pinpoint the semantic central patch on this key slice, to calculate the growing seed. To counteract the impact of noise, segmentation of the liver and spleen is conducted on superpixel images created through proxy-bridging strategy. The segmentation process is then extended to adjacent slices by applying the same methodology iteratively, culminating in the comprehensive segmentation results. Experimental results demonstrate that the proposed framework accomplishes segmentation of the liver and spleen with an average Dice Similarity Coefficient of approximately 0.93 and a Jaccard Similarity Coefficient of around 0.88. These outcomes substantiate the framework's capability to achieve performance on par with that of deep learning methods, albeit requiring less guidance information and lower GPU resources.
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Affiliation(s)
- Zhihong Chen
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Lisha Yao
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Yue Liu
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China
- School of Information Engineering, Jiangxi College of Applied Technology, Ganzhou, 341000, China
| | - Xiaorui Han
- Department of Radiology, School of Medicine, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Zhengze Gong
- Information and Data Centre, School of Medicine, Guangzhou First People's Hospital, South China University of Technology Guangdong, Guangzhou, 510180, China
| | - Jichao Luo
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Jietong Zhao
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China
| | - Gang Fang
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
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Lu S, Zhang W, Zhao H, Liu H, Wang N, Li H. Anomaly Detection for Medical Images Using Heterogeneous Auto-Encoder. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2024; 33:2770-2782. [PMID: 38551828 DOI: 10.1109/tip.2024.3381435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
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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Du Y, Wang L, Meng D, Chen B, An C, Liu H, Liu W, Xu Y, Fan Y, Feng D, Wang X, Xu X. Individualized Statistical Modeling of Lesions in Fundus Images for Anomaly Detection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1185-1196. [PMID: 36446017 DOI: 10.1109/tmi.2022.3225422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Anomaly detection in fundus images remains challenging due to the fact that fundus images often contain diverse types of lesions with various properties in locations, sizes, shapes, and colors. Current methods achieve anomaly detection mainly through reconstructing or separating the fundus image background from a fundus image under the guidance of a set of normal fundus images. The reconstruction methods, however, ignore the constraint from lesions. The separation methods primarily model the diverse lesions with pixel-based independent and identical distributed (i.i.d.) properties, neglecting the individualized variations of different types of lesions and their structural properties. And hence, these methods may have difficulty to well distinguish lesions from fundus image backgrounds especially with the normal personalized variations (NPV). To address these challenges, we propose a patch-based non-i.i.d. mixture of Gaussian (MoG) to model diverse lesions for adapting to their statistical distribution variations in different fundus images and their patch-like structural properties. Further, we particularly introduce the weighted Schatten p-norm as the metric of low-rank decomposition for enhancing the accuracy of the learned fundus image backgrounds and reducing false-positives caused by NPV. With the individualized modeling of the diverse lesions and the background learning, fundus image backgrounds and NPV are finely learned and subsequently distinguished from diverse lesions, to ultimately improve the anomaly detection. The proposed method is evaluated on two real-world databases and one artificial database, outperforming the state-of-the-art methods.
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6
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Weakly-supervised localization and classification of biomarkers in OCT images with integrated reconstruction and attention. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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7
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Du Y, Wang L, Chen B, An C, Liu H, Fan Y, Wang X, Xu X. Anomaly detection in fundus images by self-adaptive decomposition via local and color based sparse coding. BIOMEDICAL OPTICS EXPRESS 2022; 13:4261-4277. [PMID: 36032576 PMCID: PMC9408254 DOI: 10.1364/boe.461224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/19/2022] [Accepted: 07/11/2022] [Indexed: 06/15/2023]
Abstract
Anomaly detection in color fundus images is challenging due to the diversity of anomalies. The current studies detect anomalies from fundus images by learning their background images, however, ignoring the affluent characteristics of anomalies. In this paper, we propose a simultaneous modeling strategy in both sequential sparsity and local and color saliency property of anomalies are utilized for the multi-perspective anomaly modeling. In the meanwhile, the Schatten p-norm based metric is employed to better learn the heterogeneous background images, from where the anomalies are better discerned. Experiments and comparisons demonstrate the outperforming and effectiveness of the proposed method.
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Affiliation(s)
- Yuchen Du
- Department of Automation,
Shanghai Jiao Tong University, 800
Dongchuan Road, Shanghai, 200240, China
- Department of Ophthalmology, Shanghai Key
Laboratory of Ocular Fundus Diseases, Shanghai
Engineering Center for Visual Science and Photo Medicine, Shanghai
General Hospital, SJTU School of Medicine, 100 Haining
Road, Shanghai, 200080, China
- Department of Preventative Ophthalmology,
Shanghai Eye Diseases Prevention and Treatment Center,
Shanghai Eye Hospital, 380 Kangding Road, Shanghai,
200040, China
- National Clinical Research
Center for Eye Diseases, 380 Kangding Road, 200040,
China
| | - Lisheng Wang
- Department of Automation,
Shanghai Jiao Tong University, 800
Dongchuan Road, Shanghai, 200240, China
| | - Benzhi Chen
- Department of Automation,
Shanghai Jiao Tong University, 800
Dongchuan Road, Shanghai, 200240, China
| | - Chengyang An
- Department of Automation,
Shanghai Jiao Tong University, 800
Dongchuan Road, Shanghai, 200240, China
| | - Hao Liu
- Department of Automation,
Shanghai Jiao Tong University, 800
Dongchuan Road, Shanghai, 200240, China
| | - Ying Fan
- Department of Ophthalmology, Shanghai Key
Laboratory of Ocular Fundus Diseases, Shanghai
Engineering Center for Visual Science and Photo Medicine, Shanghai
General Hospital, SJTU School of Medicine, 100 Haining
Road, Shanghai, 200080, China
- Department of Preventative Ophthalmology,
Shanghai Eye Diseases Prevention and Treatment Center,
Shanghai Eye Hospital, 380 Kangding Road, Shanghai,
200040, China
- National Clinical Research
Center for Eye Diseases, 380 Kangding Road, 200040,
China
| | - Xiuying Wang
- School of Computer Science,
The University of Sydney, Sydney, NSW 2006,
Australia
| | - Xun Xu
- Department of Ophthalmology, Shanghai Key
Laboratory of Ocular Fundus Diseases, Shanghai
Engineering Center for Visual Science and Photo Medicine, Shanghai
General Hospital, SJTU School of Medicine, 100 Haining
Road, Shanghai, 200080, China
- Department of Preventative Ophthalmology,
Shanghai Eye Diseases Prevention and Treatment Center,
Shanghai Eye Hospital, 380 Kangding Road, Shanghai,
200040, China
- National Clinical Research
Center for Eye Diseases, 380 Kangding Road, 200040,
China
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