Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray
This addresses the challenge of long-tailed and noisy-label classification for computer-aided diagnosis in medical imaging, focusing on rare diseases often overlooked in existing datasets.
The paper tackles the problem of detecting rare thoracic diseases in chest X-rays by creating a new benchmark dataset (LTML-MIMIC-CXR) with 26 additional rare diseases and proposing a baseline method that improves rare disease detection.
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.