DINO-LG: A Task-Specific DINO Model for Coronary Calcium Scoring

• URL: http://arxiv.org/abs/2411.07976v7
• Date: Fri, 21 Mar 2025 17:06:08 GMT
• Title: DINO-LG: A Task-Specific DINO Model for Coronary Calcium Scoring
• Authors: Mahmut S. Gokmen, Caner Ozcan, Moneera N. Haque, Steve W. Leung, C. Seth Parker, W. Brent Seales, Cody Bumgardner,
• Abstract summary: Coronary artery disease (CAD) is one of the leading causes of mortality worldwide.<n>Traditional methods, primarily based on UNET architectures, face challenges like the scarcity of annotated CT scans containing coronary artery calcium (CAC)<n>In this study, we address these limitations by incorporating the self-supervised learning (SSL) technique of DINO.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coronary artery disease (CAD), one of the leading causes of mortality worldwide, necessitates effective risk assessment strategies, with coronary artery calcium (CAC) scoring via computed tomography (CT) being a key method for prevention. Traditional methods, primarily based on UNET architectures implemented on pre-built models, face challenges like the scarcity of annotated CT scans containing CAC and imbalanced datasets, leading to reduced performance in segmentation and scoring tasks. In this study, we address these limitations by incorporating the self-supervised learning (SSL) technique of DINO (self-distillation with no labels), which trains without requiring CAC-specific annotations, enhancing its robustness in generating distinct features. The DINO-LG model, which leverages label guidance to focus on calcified areas, achieves significant improvements, with a sensitivity of 89% and specificity of 90% for detecting CAC-containing CT slices, compared to the standard DINO model's sensitivity of 79% and specificity of 77%. Additionally, false-negative and false-positive rates are reduced by 49% and 59%, respectively, instilling greater confidence in clinicians when ruling out calcification in low-risk patients and minimizing unnecessary imaging reviews by radiologists. Further, CAC scoring and segmentation tasks are conducted using a basic UNET architecture, applied specifically to CT slices identified by the DINO-LG model as containing calcified areas. This targeted approach enhances CAC scoring accuracy by feeding the UNET model with relevant slices, significantly improving diagnostic precision, reducing both false positives and false negatives, and ultimately lowering overall healthcare costs by minimizing unnecessary tests and treatments, presenting a valuable advancement in CAD risk assessment.

Related papers

• Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging [65.83291923029985]
  Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment PCa lesions from a 200 CDI$
  arXiv  Detail & Related papers  (2025-01-15T22:23:41Z)
• DSCA: A Digital Subtraction Angiography Sequence Dataset and Spatio-Temporal Model for Cerebral Artery Segmentation [26.05396884171782]
  We introduce a DSA Sequence-based Cerebral Artery segmentation dataset (DSCA) Unlike existing DSA segmentation methods that focus only on a single frame, the proposed DSANet introduces a separate temporal encoding branch to capture dynamic vessel details across multiple frames. Extensive experiments demonstrate that DSANet outperforms other state-of-the-art methods in CA segmentation, achieving a Dice of 0.9033.
  arXiv  Detail & Related papers  (2024-06-01T07:35:21Z)
• Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
  Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
  arXiv  Detail & Related papers  (2024-03-27T16:56:14Z)
• DDSB: An Unsupervised and Training-free Method for Phase Detection in Echocardiography [37.32413956117856]
  We propose an unsupervised and training-free method to identify End-Diastolic (ED) and End-Systolic (ES) frames. By identifying anchor points and analyzing directional deformation, we effectively reduce dependence on the accuracy of initial segmentation images. Our method achieves comparable accuracy to learning-based models without their associated drawbacks.
  arXiv  Detail & Related papers  (2024-03-19T14:51:01Z)
• Automated Assessment of Critical View of Safety in Laparoscopic Cholecystectomy [51.240181118593114]
  Cholecystectomy (gallbladder removal) is one of the most common procedures in the US, with more than 1.2M procedures annually. LC is associated with an increase in bile duct injuries (BDIs), resulting in significant morbidity and mortality. In this paper, we develop deep-learning techniques to automate the assessment of critical view of safety (CVS) in LCs.
  arXiv  Detail & Related papers  (2023-09-13T22:01:36Z)
• Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients [26.50934421749854]
  We propose a multi-task deep learning model to support risk stratification and down-stream test selection. Our model achieved an Area Under the receiver operating characteristic Curve (AUC) of 0.76 in CAD risk stratification, and 0.72 AUC in predicting downstream tests.
  arXiv  Detail & Related papers  (2023-09-01T08:34:13Z)
• Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores [1.0499611180329804]
  The aim is to improve the workflow of prostate cancer detection and diagnosis. The top-performing model is the Attention R2U-Net, achieving a mean Intersection over Union (IoU) of 76.3% and Dice Similarity Coefficient (DSC) of 85% for segmenting all zones.
  arXiv  Detail & Related papers  (2023-08-09T01:38:58Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach [1.180462901068842]
  Focal cortical dysplasia (FCD) is a leading cause of drug-resistant epilepsy, which can be cured by surgery. "Ground truth" manual lesion masks are therefore expensive, limited and have large inter-rater variability. We propose to approach the problem as semantic segmentation using graph convolutional networks (GCN), which allows our model to learn spatial relationships between brain regions.
  arXiv  Detail & Related papers  (2023-06-02T08:56:56Z)
• Generative Models for Reproducible Coronary Calcium Scoring [3.1746159467221253]
  Coronary artery calcium (CAC) score is a strong and independent predictor of coronary heart disease (CHD) events. CAC scoring suffers from limited interscan due to clinical definition requiring application of a fixed intensity level threshold for segmentation of calcifications. We propose a CAC method that does not require a threshold for segmentation of CAC.
  arXiv  Detail & Related papers  (2022-05-24T10:59:32Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Nested-block self-attention for robust radiotherapy planning segmentation [3.2541650155921142]
  Deep convolutional networks have been widely studied for head and neck (HN) organs at risk (OAR) segmentation. Their use for routine clinical treatment planning is limited by a lack of robustness to imaging artifacts, low soft tissue contrast on CT, and the presence of abnormal anatomy. We developed a computationally efficient nested block self-attention (NBSA) method that can be combined with any convolutional network.
  arXiv  Detail & Related papers  (2021-02-26T15:28:47Z)
• 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)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• 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 Lesion Segmentation on CT Scans using Co-Segmentation [18.58056402884405]
  Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth. Current practices rely on an imprecise substitute called response evaluation criteria in solid tumors. This paper proposes a convolutional neural network based weakly-supervised lesion segmentation method.
  arXiv  Detail & Related papers  (2020-01-23T15:15:53Z)

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.