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

Yuanhong Chen, Fengbei Liu, Hu Wang, Chong Wang, Yuyuan Liu, Yu Tian, Gustavo Carneiro; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 21284-21295


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 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 multi-class noisy-label learning methods cannot be easily adapted. In this paper, we propose a new method designed for noisy multi-label CXR learning, which detects and smoothly re-labels noisy samples from the dataset to be used in the training of 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 language models from multi-label image annotations. Our experiments on 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, including a new benchmark that we propose to systematically assess noisy multi-label methods.

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@InProceedings{Chen_2023_ICCV, author = {Chen, Yuanhong and Liu, Fengbei and Wang, Hu and Wang, Chong and Liu, Yuyuan and Tian, Yu and Carneiro, Gustavo}, title = {BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {21284-21295} }