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APAN: Anti-curriculum Pseudo-Labelling and Adversarial Noises Training for Semi-supervised Medical Image Classification

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15044))

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Abstract

Pseudo-label semi-supervised learning (SSL) has gained extensive adoption in medical image analysis, with many methods allocating pseudo-labels to unlabeled samples through probability thresholds. However, this approach is prone to introducing incorrect pseudo-labels, leading to confirmation bias and it cannot effectively handle multi-class and multi-label problems. Additionally, the unselected samples are often ignored and not fully utilized. To address these issues, a novel SSL method named APAN is proposed in this paper, utilizing anti-curriculum learning and adversarial noise training. Instead of employing a probability threshold approach, we opt to select high-information samples for pseudo-labeling, thereby empowering our model to effectively address multi-label and multi-class problems. Moreover, we introduce adversarial noise to smooth the decision boundary on unselected samples rather than discarding them outright. We conducted extensive experiments on two public medical image datasets, Chest X-Ray14 and ISIC2018, demonstrating the feasibility of our approach.

* Supported by Natural Science Foundation of Sichuan Provinceunder Grant (2022NSFSC0552, 2023NSFSC1397), National Natural Science Foundation of China (62006165), and Research on the Gene Theory and Application of Pattern Composition in Handmade Fabrics of Ethnic Minorities in Xinjiang(20BMZ092)

Junfan Chen and Jun Yang—Authors contribute equally to this work.

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Author information

Authors and Affiliations

  1. College of Computer Science, Sichuan Normal University, Chengdu, China

    Junfan Chen, Jun Yang, Anfei Fan, Jinyin Jia, Chiyu Zhang & Wei Li

  2. Visual Computing and Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu, China

    Junfan Chen, Jun Yang, Anfei Fan, Jinyin Jia, Chiyu Zhang & Wei Li

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  1. Junfan Chen
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Correspondence to Jun Yang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Urumqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Urumqi, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Chen, J., Yang, J., Fan, A., Jia, J., Zhang, C., Li, W. (2025). APAN: Anti-curriculum Pseudo-Labelling and Adversarial Noises Training for Semi-supervised Medical Image Classification. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15044. Springer, Singapore. https://doi.org/10.1007/978-981-97-8496-7_12

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  • DOI: https://doi.org/10.1007/978-981-97-8496-7_12

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