Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray
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.
Code (1)
Tasks
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognition
In real-world scenarios, collected and annotated data often exhibit the characteristics of multiple classes and long-tailed distribution. Additionally, label noise is inevitable in large-scale annotations and hinders the…
Learning with noisy labelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRepresentation LearningExtracting Clean and Balanced Subset for Noisy Long-tailed Classification
Real-world datasets usually are class-imbalanced and corrupted by label noise. To solve the joint issue of long-tailed distribution and label noise, most previous works usually aim to design a noise detector to distingui…
Pseudo LabelCombating Noisy Labels in Long-Tailed Image Classification
Most existing methods that cope with noisy labels usually assume that the class distributions are well balanced, which has insufficient capacity to deal with the practical scenarios where training samples have imbalanced…
Classificationimage-classificationImage ClassificationCXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray
The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease classification and enhances the measurab…
ClassificationDiagnosticLung Disease ClassificationZero-shot Generalization+1Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss
The success of deep learning depends on large-scale and well-curated training data, while data in real-world applications are commonly long-tailed and noisy. Many methods have been proposed to deal with long-tailed data …