Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray

• URL: http://arxiv.org/abs/2311.17334v1
• Date: Wed, 29 Nov 2023 03:21:34 GMT
• Title: Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray
• Authors: Haoran Lai and Qingsong Yao and Zhiyang He and Xiaodong Tao and S Kevin Zhou
• Abstract summary: Chest X-rays (CXR) often reveal rare diseases, demanding precise diagnosis. We present a novel benchmark for long-tailed multi-label classification in CXRs, encapsulating both common and rare thoracic diseases. Our approach includes developing the "LTML-MIMIC-CXR" dataset, an augmentation of MIMIC-CXR with 26 additional rare diseases.
• Score: 17.385189528192118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chest X-rays (CXR) often reveal rare diseases, demanding precise diagnosis. However, current computer-aided diagnosis (CAD) methods focus on common diseases, leading to inadequate detection of rare conditions due to the absence of comprehensive datasets. To overcome this, we present a novel benchmark for long-tailed multi-label classification in CXRs, encapsulating both common and rare thoracic diseases. Our approach includes developing the "LTML-MIMIC-CXR" dataset, an augmentation of MIMIC-CXR with 26 additional rare diseases. We propose a baseline method for this classification challenge, integrating adaptive negative regularization to address negative logits' over-suppression in tail classes, and a large loss reconsideration strategy for correcting noisy labels from automated annotations. Our evaluation on LTML-MIMIC-CXR demonstrates significant advancements in rare disease detection. This work establishes a foundation for robust CAD methods, achieving a balance in identifying a spectrum of thoracic diseases in CXRs. Access to our code and dataset is provided at:https://github.com/laihaoran/LTML-MIMIC-CXR.

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