A 3D deep learning classifier and its explainability when assessing coronary artery disease

• URL: http://arxiv.org/abs/2308.00009v2
• Date: Tue, 26 Nov 2024 19:40:27 GMT
• Title: A 3D deep learning classifier and its explainability when assessing coronary artery disease
• Authors: Wing Keung Cheung, Jeremy Kalindjian, Robert Bell, Arjun Nair, Leon J. Menezes, Riyaz Patel, Simon Wan, Kacy Chou, Jiahang Chen, Ryo Torii, Rhodri H. Davies, James C. Moon, Daniel C. Alexander, Joseph Jacob,
• Abstract summary: Early detection and diagnosis of coronary artery disease could save lives and reduce healthcare costs.<n>Most current approaches utilise deep learning methods but require centerline extraction and multi-planar reconstruction.<n>Our proposed approach outperforms the state-of-the-art models by 21.43% in terms of classification accuracy.
• Score: 2.749052158388996
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Early detection and diagnosis of coronary artery disease (CAD) could save lives and reduce healthcare costs. The current clinical practice is to perform CAD diagnosis through analysing medical images from computed tomography coronary angiography (CTCA). Most current approaches utilise deep learning methods but require centerline extraction and multi-planar reconstruction. These indirect methods are not designed in a clinician-friendly manner, and they complicate the interventional procedure. Furthermore, the current deep learning methods do not provide exact explainability and limit the usefulness of these methods to be deployed in clinical settings. In this study, we first propose a 3D Resnet-50 deep learning model to directly classify normal subjects and CAD patients on CTCA images, then we demonstrate a 2D modified U-Net model can be subsequently employed to segment the coronary arteries. Our proposed approach outperforms the state-of-the-art models by 21.43% in terms of classification accuracy. The classification model with focal loss provides a better and more focused heat map, and the segmentation model provides better explainability than the classification-only model. The proposed holistic approach not only provides a simpler and clinician-friendly solution but also good classification accuracy and exact explainability for CAD diagnosis.

Related papers

• Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification [1.091626241764448]
  Diabetic Retinopathy (DR) is a primary cause of blindness, necessitating early detection and diagnosis. We develop an advanced cross-learning DR classification method leveraging transfer learning and cross-attention mechanisms. Our experiments, utilizing two public datasets, demonstrate a superior accuracy of 94.6%, surpassing current state-of-the-art methods by 4.4%.
  arXiv  Detail & Related papers  (2024-11-06T02:23:38Z)
• NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections by Neural Implicit Representation [2.1771042711033997]
  2D x-ray invasive coronary angiography remains as the most widely adopted imaging modality for CVDs diagnosis. Due to the radiation limit, in general only two angiographic projections are acquired, providing limited information of the vessel geometry. We propose a self-supervised deep learning method called NeCA, which is based on implicit neural representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer.
  arXiv  Detail & Related papers  (2024-09-06T20:08:21Z)
• Deep Learning-based 3D Coronary Tree Reconstruction from Two 2D Non-simultaneous X-ray Angiography Projections [1.9929038355503754]
  Cardiovascular diseases (CVDs) are the most common cause of death worldwide. Invasive x-ray coronary angiography (ICA) is one of the most important imaging modalities for the diagnosis of CVDs. ICA typically acquires only two 2D projections, which makes the 3D geometry of coronary vessels difficult to interpret. We propose a novel deep learning pipeline to correct the non-rigid cardiac and respiratory motions between non-simultaneous projections.
  arXiv  Detail & Related papers  (2024-07-19T18:18:17Z)
• UDCR: Unsupervised Aortic DSA/CTA Rigid Registration Using Deep Reinforcement Learning and Overlap Degree Calculation [12.201553863670155]
  We propose an unsupervised method, UDCR, for aortic DSA/CTA rigid registration based on deep reinforcement learning. We manually annotated 61 pairs of aortic DSA/CTA for algorithm evaluation. The results indicate that the proposed UDCR achieved a Mean Absolute Error (MAE) of 2.85 mm in translation and 4.35deg in rotation.
  arXiv  Detail & Related papers  (2024-03-09T01:18:32Z)
• CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography [1.5404452377809545]
  Coronary artery disease (CAD) remains the leading cause of death globally. Deep learning classification methods are well-developed in other areas of medical imaging. One of the most important reasons is the lack of available and high-quality open-access datasets.
  arXiv  Detail & Related papers  (2024-02-01T13:03:13Z)
• SSASS: Semi-Supervised Approach for Stenosis Segmentation [9.767759441883008]
  The complexity of coronary artery structures combined with the inherent noise in X-ray images poses a considerable challenge to this task. We introduce a semi-supervised approach for cardiovascular stenosis segmentation. Our approach demonstrated an exceptional performance in the Automatic Region-based Coronary Artery Disease diagnostics.
  arXiv  Detail & Related papers  (2023-11-17T02:01:19Z)
• Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning [3.656984996633334]
  This study introduces an end-to-end machine learning solution developed as part of our solution for the MICCAI 2023 Automatic Region-based Coronary Artery Disease diagnostics. It aims to benchmark solutions for multivessel coronary artery segmentation and potential stenotic lesion localisation from X-ray coronary angiograms. Our solution achieved a mean F1 score of $37.69%$ for coronary artery segmentation, and $39.41%$ for stenosis localisation, positioning our team in the 5th position on both leaderboards.
  arXiv  Detail & Related papers  (2023-10-27T08:03:12Z)
• On the Localization of Ultrasound Image Slices within Point Distribution Models [84.27083443424408]
  Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US) Longitudinal tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. We present a framework for automated US image slice localization within a 3D shape representation.
  arXiv  Detail & Related papers  (2023-09-01T10:10:46Z)
• 3D Coronary Vessel Reconstruction from Bi-Plane Angiography using Graph Convolutional Networks [1.8244763402770727]
  3DAngioNet is a novel deep learning (DL) system that enables rapid 3D vessel mesh reconstruction using 2D XCA images from two views. Our approach learns a coarse mesh template using an EfficientB3-UNet segmentation network and projection geometries, and deforms it using a graph convolutional network.
  arXiv  Detail & Related papers  (2023-02-28T17:46:25Z)
• A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans [5.5957919486531935]
  In COVID-19 computed tomography (CT) images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis. Some researchers propose the relevant DL models which can replace professional diagnostic specialists in clinics when lacking expertise. Our model achieves 94.52% accuracy in the classification of lung lesions by 3 types: COVID, Pneumonia and Normal.
  arXiv  Detail & Related papers  (2022-08-04T22:14:18Z)
• Explainable multiple abnormality classification of chest CT volumes with AxialNet and HiResCAM [89.2175350956813]
  We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images. We propose a multiple instance learning convolutional neural network, AxialNet, that allows identification of top slices for each abnormality. We then aim to improve the model's learning through a novel mask loss that leverages HiResCAM and 3D allowed regions.
  arXiv  Detail & Related papers  (2021-11-24T01:14:33Z)
• Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities [60.079782224958414]
  We will present and discuss opportunities and challenges brought by a new deep learning method by AUC (aka underlinebf Deep underlinebf AUC classification)
  arXiv  Detail & Related papers  (2021-11-01T15:31:32Z)
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae [54.751251046196494]
  We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices. SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
  arXiv  Detail & Related papers  (2021-10-28T12:59:06Z)
• 3D Convolutional Neural Networks for Stalled Brain Capillary Detection [72.21315180830733]
  Brain vasculature dysfunctions such as stalled blood flow in cerebral capillaries are associated with cognitive decline and pathogenesis in Alzheimer's disease. Here, we describe a deep learning-based approach for automatic detection of stalled capillaries in brain images based on 3D convolutional neural networks. In this setting, our approach outperformed other methods and demonstrated state-of-the-art results, achieving 0.85 Matthews correlation coefficient, 85% sensitivity, and 99.3% specificity.
  arXiv  Detail & Related papers  (2021-04-04T20:30:14Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans [72.04652116817238]
  We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification. We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
  arXiv  Detail & Related papers  (2021-01-14T03:45:01Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability [76.64661091980531]
  People with diabetes are at risk of developing diabetic retinopathy (DR) Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading. This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
  arXiv  Detail & Related papers  (2020-08-22T07:48:04Z)
• Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet [74.22397862400177]
  We propose a novel catheter segmentation approach, which requests fewer annotations than the supervised learning method. Our scheme considers a deep Q learning as the pre-localization step, which avoids voxel-level annotation. With the detected catheter, patch-based Dual-UNet is applied to segment the catheter in 3D volumetric data.
  arXiv  Detail & Related papers  (2020-06-25T21:10:04Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)
• Weakly-supervised 3D coronary artery reconstruction from two-view angiographic images [4.722039838364292]
  We propose an adversarial and generative way to reconstruct three dimensional coronary artery models. With 3D fully supervised learning and 2D weakly supervised learning schemes, we obtained reconstruction accuracies that outperform state-of-art techniques.
  arXiv  Detail & Related papers  (2020-03-26T11:41:38Z)

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.