2021/06/03 by Hong-Yu Zhou, Zhou, Hong-Yu, Chengdi Wang +11 · 2 citations
Computer Science · Medicine · #Advanced Neural Network Applications #Artificial intelligence #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Consistency (knowledge bases) #Detector #FOS: Computer and information sciences #Image (mathematics) #Machine learning #Medical Image Segmentation Techniques #Medical imaging #Object detection #Pattern recognition (psychology) #Segmentation #cs.CV
paper · pdf · doi:10.48550/arxiv.2106.01544
published in arXiv (Cornell University) (Cornell University) · Accepted by Medical Image Analysis
arxiv created 2021/06/03 · openalex publication_date 2021/06/03 · arxiv updated 2021/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.