BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification

• URL: http://arxiv.org/abs/2203.01937v5
• Date: Wed, 9 Aug 2023 08:16:47 GMT
• Title: BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification
• Authors: Yuanhong Chen, Fengbei Liu, Hu Wang, Chong Wang, Yu Tian, Yuyuan Liu, Gustavo Carneiro
• Abstract summary: New medical imaging classification problems may need to rely on machine-generated noisy labels extracted from radiology reports. Current noisy-label learning methods designed for multi-class problems cannot be easily adapted. We propose a new method designed for the noisy multi-label CXR learning, which detects and smoothly re-labels samples from the dataset.
• Score: 25.76256302330625
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning methods have shown outstanding classification accuracy in medical imaging problems, which is largely attributed to the availability of large-scale datasets manually annotated with clean labels. However, given the high cost of such manual annotation, new medical imaging classification problems may need to rely on machine-generated noisy labels extracted from radiology reports. Indeed, many Chest X-ray (CXR) classifiers have already been modelled from datasets with noisy labels, but their training procedure is in general not robust to noisy-label samples, leading to sub-optimal models. Furthermore, CXR datasets are mostly multi-label, so current noisy-label learning methods designed for multi-class problems cannot be easily adapted. In this paper, we propose a new method designed for the noisy multi-label CXR learning, which detects and smoothly re-labels samples from the dataset, which is then used to train common multi-label classifiers. The proposed method optimises a bag of multi-label descriptors (BoMD) to promote their similarity with the semantic descriptors produced by BERT models from the multi-label image annotation. Our experiments on diverse noisy multi-label training sets and clean testing sets show that our model has state-of-the-art accuracy and robustness in many CXR multi-label classification benchmarks.

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