SSMD: Semi-Supervised medical image detection with adaptive consistency and heterogeneous perturbation
•We propose a semi-supervised medical image detector with a novel adaptive consistency cost function which takes into account the confidence of proposals at each spatial position.•We develop novel heterogeneous perturbation strategies that consist of two novel components: a noisy residual block for...
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| Vydáno v: | Medical image analysis Ročník 72; s. 102117 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
01.08.2021
Elsevier BV |
| Témata: | |
| ISSN: | 1361-8415, 1361-8423, 1361-8423 |
| On-line přístup: | Získat plný text |
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| Abstract | •We propose a semi-supervised medical image detector with a novel adaptive consistency cost function which takes into account the confidence of proposals at each spatial position.•We develop novel heterogeneous perturbation strategies that consist of two novel components: a noisy residual block for feature space, and an instance-level adversarial perturbation strategy for image space. The proposed heterogeneous perturbation strategies can improve the detection accuracy by enhancing the robustness of image features.•We experimentally verify the effectiveness of the proposed modules. The experiments suggest that the proposed detector outperforms the existing state-of-the-arts considerably.
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Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis. In this paper, we propose a novel Semi-Supervised Medical image Detector (SSMD). The motivation behind SSMD is to provide free yet effective supervision for unlabeled data, by regularizing the predictions at each position to be consistent. To achieve the above idea, we develop a novel adaptive consistency cost function to regularize different components in the predictions. Moreover, we introduce heterogeneous perturbation strategies that work in both feature space and image space, so that the proposed detector is promising to produce powerful image representations and robust predictions. Extensive experimental results show that the proposed SSMD achieves the state-of-the-art performance at a wide range of settings. We also demonstrate the strength of each proposed module with comprehensive ablation studies. |
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| AbstractList | •We propose a semi-supervised medical image detector with a novel adaptive consistency cost function which takes into account the confidence of proposals at each spatial position.•We develop novel heterogeneous perturbation strategies that consist of two novel components: a noisy residual block for feature space, and an instance-level adversarial perturbation strategy for image space. The proposed heterogeneous perturbation strategies can improve the detection accuracy by enhancing the robustness of image features.•We experimentally verify the effectiveness of the proposed modules. The experiments suggest that the proposed detector outperforms the existing state-of-the-arts considerably.
[Display omitted]
Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis. In this paper, we propose a novel Semi-Supervised Medical image Detector (SSMD). The motivation behind SSMD is to provide free yet effective supervision for unlabeled data, by regularizing the predictions at each position to be consistent. To achieve the above idea, we develop a novel adaptive consistency cost function to regularize different components in the predictions. Moreover, we introduce heterogeneous perturbation strategies that work in both feature space and image space, so that the proposed detector is promising to produce powerful image representations and robust predictions. Extensive experimental results show that the proposed SSMD achieves the state-of-the-art performance at a wide range of settings. We also demonstrate the strength of each proposed module with comprehensive ablation studies. Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis. In this paper, we propose a novel Semi-Supervised Medical image Detector (SSMD). The motivation behind SSMD is to provide free yet effective supervision for unlabeled data, by regularizing the predictions at each position to be consistent. To achieve the above idea, we develop a novel adaptive consistency cost function to regularize different components in the predictions. Moreover, we introduce heterogeneous perturbation strategies that work in both feature space and image space, so that the proposed detector is promising to produce powerful image representations and robust predictions. Extensive experimental results show that the proposed SSMD achieves the state-of-the-art performance at a wide range of settings. We also demonstrate the strength of each proposed module with comprehensive ablation studies.Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis. In this paper, we propose a novel Semi-Supervised Medical image Detector (SSMD). The motivation behind SSMD is to provide free yet effective supervision for unlabeled data, by regularizing the predictions at each position to be consistent. To achieve the above idea, we develop a novel adaptive consistency cost function to regularize different components in the predictions. Moreover, we introduce heterogeneous perturbation strategies that work in both feature space and image space, so that the proposed detector is promising to produce powerful image representations and robust predictions. Extensive experimental results show that the proposed SSMD achieves the state-of-the-art performance at a wide range of settings. We also demonstrate the strength of each proposed module with comprehensive ablation studies. Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis. In this paper, we propose a novel Semi-Supervised Medical image Detector (SSMD). The motivation behind SSMD is to provide free yet effective supervision for unlabeled data, by regularizing the predictions at each position to be consistent. To achieve the above idea, we develop a novel adaptive consistency cost function to regularize different components in the predictions. Moreover, we introduce heterogeneous perturbation strategies that work in both feature space and image space, so that the proposed detector is promising to produce powerful image representations and robust predictions. Extensive experimental results show that the proposed SSMD achieves the state-of-the-art performance at a wide range of settings. We also demonstrate the strength of each proposed module with comprehensive ablation studies. |
| ArticleNumber | 102117 |
| Author | Li, Haofeng Zhou, Hong-Yu Li, Weimin Wang, Chengdi Wang, Gang Zhang, Shu Yu, Yizhou |
| Author_xml | – sequence: 1 givenname: Hong-Yu surname: Zhou fullname: Zhou, Hong-Yu organization: Department of Respiratory and Critical Care Medicine, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, P.R. China – sequence: 2 givenname: Chengdi surname: Wang fullname: Wang, Chengdi organization: Department of Respiratory and Critical Care Medicine, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, P.R. China – sequence: 3 givenname: Haofeng orcidid: 0000-0001-9120-9843 surname: Li fullname: Li, Haofeng organization: Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen 518000, P.R. China – sequence: 4 givenname: Gang surname: Wang fullname: Wang, Gang organization: Department of Respiratory and Critical Care Medicine, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, P.R. China – sequence: 5 givenname: Shu orcidid: 0000-0002-2049-0970 surname: Zhang fullname: Zhang, Shu organization: AI Lab, Deepwise Healthcare, Beijing 100080, P.R. China – sequence: 6 givenname: Weimin surname: Li fullname: Li, Weimin email: weimi003@scu.edu.cn organization: Department of Respiratory and Critical Care Medicine, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, P.R. China – sequence: 7 givenname: Yizhou orcidid: 0000-0002-0470-5548 surname: Yu fullname: Yu, Yizhou email: yizhouy@acm.org organization: Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong |
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| SubjectTerms | Ablation Consistency Cost function Image analysis Image classification Image detection Image processing Image segmentation Lesion detection Medical imaging Motivation Nuclei detection Object recognition Performance enhancement Perturbation Predictions Semi-Supervised learning |
| Title | SSMD: Semi-Supervised medical image detection with adaptive consistency and heterogeneous perturbation |
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