A transformation uncertainty and multi-scale contrastive learning-based semi-supervised segmentation method for oral cavity-derived cancer.

Journal: Frontiers in oncology
Published Date: Jan 1, 2025

Abstract

OBJECTIVES: Oral cavity-derived cancer pathological images (OPI) are crucial for diagnosing oral squamous cell carcinoma (OSCC), but existing deep learning methods for OPI segmentation rely heavily on large, accurately labeled datasets, which are labor- and resource-intensive to obtain. This paper presents a semi-supervised segmentation method for OPI to mitigate the limitations of scarce labeled data by leveraging both labeled and unlabeled data.

Authors

  • Ran Wang
    Department of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
  • Chengqi Lyu
    Department of Stomatology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Lvfeng Yu
    Department of Stomatology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

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