A Simple and Provable Approach for Learning on Noisy Labeled Multi-modal Medical Images
Abstract: Deep learning for medical image classification needs large amounts of carefully labeled data with the aid of domain experts. However, data labeling is vulnerable to noises, which may degrade the ac curacy of classifiers. Given the cost of medical data collection and annotation, it is highly desirable for methods that can effectively utilize noisy labeled data. In addition, efficiency and universality are essential for noisy label training, which requires further research. To address the lack of high-quality labeled medical data and meet algorithm efficiency requirements for clinical application, we propose a simple yet effective approach for multi-field medical images to utilize noisy data, named Pseudo-T correction. Specifically, we design a noisy label filter to divide the training data into clean and noisy samples. Then, we estimate a transition matrix that corrects model predictions based on the partitions of clean and noisy data samples. However, if the model overfits noisy data, noisy samples become more difficult to detect in the filtering step, resulting in inaccurate transition matrix estimation. Therefore, we employ gradient disparity as an effective criterion to decide whether or not to refine the transition matrix in the model’s further training steps. The novel design enables us to build more accurate machine-learning models by leveraging noisy labels. We demonstrate that our method outperforms the state-of-the-art methods on three public medical datasets and achieves superior computational efficiency over the alternatives.
Primary Subject Area: [Content] Vision and Language
Relevance To Conference: This paper belongs to the topic of multimedia analysis and is dedicated to the research of image data processing and analysis. It mainly solves the problem of realizing intelligent diagnosis of medical imaging data under noise labels.
Supplementary Material: zip
Submission Number: 3196
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