Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

• URL: http://arxiv.org/abs/2310.16112v2
• Date: Mon, 1 Apr 2024 20:18:02 GMT
• Title: Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
• Authors: Gregory Holste, Yiliang Zhou, Song Wang, Ajay Jaiswal, Mingquan Lin, Sherry Zhuge, Yuzhe Yang, Dongkyun Kim, Trong-Hieu Nguyen-Mau, Minh-Triet Tran, Jaehyup Jeong, Wongi Park, Jongbin Ryu, Feng Hong, Arsh Verma, Yosuke Yamagishi, Changhyun Kim, Hyeryeong Seo, Myungjoo Kang, Leo Anthony Celi, Zhiyong Lu, Ronald M. Summers, George Shih, Zhangyang Wang, Yifan Peng,
• Abstract summary: Many real-world image recognition problems, such as diagnostic medical imaging exams, are emerging. Diagnose is both a long-tailed and multi-label problem, as patients often present with multiple findings. We synthesize common themes, providing recommendations for long-tailed, multi-label medical image classification.
• Score: 59.323306639144526
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification.

Related papers

• MONICA: Benchmarking on Long-tailed Medical Image Classification [14.547590439984807]
  Long-tailed learning is considered to be an extremely challenging problem in data imbalance learning. We build a unified, well-structured called Medical OpeN-source Long-taIled ClassifiCAtion (MONICA) It implements over 30 methods developed in relevant fields and evaluated on 12 long-tailed medical datasets covering 6 medical domains.
  arXiv  Detail & Related papers  (2024-10-02T20:21:46Z)
• Semi-Supervised Disease Classification based on Limited Medical Image Data [9.633774896301436]
  This paper introduces a novel generative model inspired by H"older divergence for semi-supervised disease classification. We conduct experiments on five benchmark datasets commonly used in PU medical learning. Our approach achieves state-of-the-art performance on all five disease classification benchmarks.
  arXiv  Detail & Related papers  (2024-05-07T13:11:08Z)
• Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray [17.385189528192118]
  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.
  arXiv  Detail & Related papers  (2023-11-29T03:21:34Z)
• Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning [48.29204631769816]
  We re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification. Our experimental results show that models considering disease severity and uncertainty outperform previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-06T19:19:41Z)
• Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study [75.05049024176584]
  We present a benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays. We focus on learning from naturally distributed chest X-ray data, optimizing classification accuracy over not only the common "head" classes, but also the rare yet critical "tail" classes. The benchmark consists of two chest X-ray datasets for 19- and 20-way thorax disease classification, containing classes with as many as 53,000 and as few as 7 labeled training images.
  arXiv  Detail & Related papers  (2022-08-29T04:34:15Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.