cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations

• URL: http://arxiv.org/abs/2307.07254v1
• Date: Fri, 14 Jul 2023 10:05:37 GMT
• Title: cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations
• Authors: Silvia D. Almeida, Carsten T. L\"uth, Tobias Norajitra, Tassilo Wald, Marco Nolden, Paul F. Jaeger, Claus P. Heussel, J\"urgen Biederer, Oliver Weinheimer, Klaus Maier-Hein
• Abstract summary: We propose cOOpD: heterogeneous pathological regions are detected as Out-of-Distribution (OOD) from normal homogeneous lung regions. A generative model then learns the distribution of healthy representations and identifies abnormalities (stemming from COPD) as deviations. We show that cOOpD achieves the best performance on two public datasets, with an increase of 8.2% and 7.7% in terms of AUROC.
• Score: 0.6733204971296001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classification of heterogeneous diseases is challenging due to their complexity, variability of symptoms and imaging findings. Chronic Obstructive Pulmonary Disease (COPD) is a prime example, being underdiagnosed despite being the third leading cause of death. Its sparse, diffuse and heterogeneous appearance on computed tomography challenges supervised binary classification. We reformulate COPD binary classification as an anomaly detection task, proposing cOOpD: heterogeneous pathological regions are detected as Out-of-Distribution (OOD) from normal homogeneous lung regions. To this end, we learn representations of unlabeled lung regions employing a self-supervised contrastive pretext model, potentially capturing specific characteristics of diseased and healthy unlabeled regions. A generative model then learns the distribution of healthy representations and identifies abnormalities (stemming from COPD) as deviations. Patient-level scores are obtained by aggregating region OOD scores. We show that cOOpD achieves the best performance on two public datasets, with an increase of 8.2% and 7.7% in terms of AUROC compared to the previous supervised state-of-the-art. Additionally, cOOpD yields well-interpretable spatial anomaly maps and patient-level scores which we show to be of additional value in identifying individuals in the early stage of progression. Experiments in artificially designed real-world prevalence settings further support that anomaly detection is a powerful way of tackling COPD classification.

Related papers

• Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly Detection [34.14012444375776]
  We propose a Dual-space Uncertainty Estimation framework for medical anomaly detection. To balance agreement and disagreement for anomaly detection, we propose Redundancy-Aware Repulsion (RAR) We also develop Dual-Space Uncertainty (DSU) which utilizes the ensemble's uncertainty in input and output spaces.
  arXiv  Detail & Related papers  (2024-09-26T02:47:41Z)
• A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection [41.454028276986946]
  We propose a novel framework Two-Stage Generative Model (TSGM) to improve brain tumor detection and segmentation. CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior. VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide.
  arXiv  Detail & Related papers  (2023-11-06T12:58:26Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles [52.77024349608834]
  We analyze the influence of replacing a DCNN with a state-of-the-art face recognition approach, iResNet with ArcFace. Our proposed ensemble model achieves state-of-the-art performance on both seen and unseen disorders.
  arXiv  Detail & Related papers  (2022-11-12T23:28:54Z)
• Adversarial Transformer for Repairing Human Airway Segmentation [7.176060570019899]
  This paper presents a patch-scale adversarial-based refinement network that takes in preliminary segmentation along with original CT images and outputs a refined mask of the airway structure. The results are quantitatively evaluated by seven metrics and achieved more than a 15% rise in detected length ratio and detected branch ratio.
  arXiv  Detail & Related papers  (2022-10-21T15:20:08Z)
• Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection [81.07346419422605]
  Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. We propose a novel hierarchical semi-supervised contrastive learning framework, for contamination-resistant anomaly detection.
  arXiv  Detail & Related papers  (2022-07-24T18:49:26Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria [83.83783947027392]
  This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated.
  arXiv  Detail & Related papers  (2021-05-06T10:21:28Z)
• Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images [23.79184121052212]
  Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images. We propose a novel self-supervised representation learning method, called Constrained Contrastive Distribution learning for anomaly detection (CCD) Our method outperforms current state-of-the-art UAD approaches on three different colonoscopy and fundus screening datasets.
  arXiv  Detail & Related papers  (2021-03-05T01:56:58Z)
• Contralaterally Enhanced Networks for Thoracic Disease Detection [120.60868136876599]
  There exist many similar structures in the left and right parts of the chest, such as ribs, lung fields and bronchial tubes. This kind of similarities can be used to identify diseases in chest X-rays, according to the experience of broad-certificated radiologists. We propose a deep end-to-end module to exploit the contralateral context information for enhancing feature representations of disease proposals.
  arXiv  Detail & Related papers  (2020-10-09T10:15:26Z)

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.