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Early Detection of Heart Disease Using Deep Learning Model 8

Early Detection of Heart Disease Using Deep Learning Model

引用

8th international conference on Smart Structures and Systems, ICSSS 2022

作者： Deepika, R. Srikaanth, P. Balaji Pitchai, R. Srm Institute of Science and Technology Kattankulathur Technology School of Computing Tamilnadu India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India B v Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9781665497619

Identifying and detecting the early signs of diseases is difficult due to asymptomatic conditions. Detecting the signs early gives the patient a higher chance of surviving. there are several technologies to identify the heart diseases for timely treatment. So far, different techniques of machine learning is used to predict heart abnormalities. Machine learning models trained by large amounts of data in the healthcare field contain a lot of medical images that enable the medical professionals to identify even minimal changes with a high level of accuracy. this paper presents the deep learning technique used to predict the heart abnormalities. Deep learning technique helps in processing the image data through various tests which include electrocardiogram, echocardiogram etc., that detects the abnormalities in the heart. © 2022 IEEE.

关键词： Heart Deep learning medical services Feature extraction Data models Intelligent structures medical diagnostic imaging

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MaxStyle: Adversarial Style Composition for Robust medical image Segmentation 25th

MaxStyle: Adversarial Style Composition for Robust Medical I...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Chen, Chen Li, Zeju Ouyang, Cheng Sinclair, Matthew Bai, Wenjia Rueckert, Daniel Imperial Coll London Dept Comp BioMedIA Grp London England HeartFlow Mountain View CA USA Imperial Coll London Data Sci Inst London England Imperial Coll London Dept Brain Sci London England Tech Univ Munich Klinikum Rechts Isar Munich Germany

ISBN: (纸本)9783031164439;9783031164422

Convolutional neural networks (CNNs) have achieved remarkable segmentation accuracy on benchmark datasets where training and test sets are from the same domain, yet their performance can degrade significantly on unseen domains, which hinders the deployment of CNNs in many clinical scenarios. Most existing works improve model out-of-domain (OOD) robustness by collecting multi-domain datasets for training, which is expensive and may not always be feasible due to privacy and logistical issues. In this work, we focus on improving model robustness using a single-domain dataset only. We propose a novel data augmentation framework called MaxStyle, which maximizes the effectiveness of style augmentation for model OOD performance. It attaches an auxiliary style-augmented image decoder to a segmentation network for robust feature learning and data augmentation. Importantly, MaxStyle augments data with improved image style diversity and hardness, by expanding the style space with noise and searching for the worst-case style composition of latent features via adversarial training With extensive experiments on multiple public cardiac and prostate MR datasets, we demonstrate that MaxStyle leads to significantly improved out-ofdistribution robustness against unseen corruptions as well as common distribution shifts across multiple, different, unseen sites and unknown image sequences under both low- and high-training data settings. the code can be found at https://***/cherise215/MaxStyle.

关键词： Convolutional neural networks

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Many-MobileNet: Multi-model Augmentation for Robust Retinal Disease Classification 1st

Many-MobileNet: Multi-model Augmentation for Robust Retinal...

引用

1st MICCAI Challenge on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy, UWF4DR 2024, Held in Conjunction with 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Wang, Hao Zhu, Wenhui Dong, Xuanzhao Chen, Yanxi Li, Xin Qiu, Peijie Chen, Xiwen Vasa, Vamsi Krishna Xiong, Yujian Dumitrascu, Oana M. Razi, Abolfazl Wang, Yalin School of Computing Clemson University ClemsonSC United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States McKeley School of Engineering Washington University in St. Louis St. LouisMO United States Department of Neurology Mayo Clinic RochesterAZ United States

ISBN: (纸本)9783031893872

In this work, we propose Many-MobileNet − an efficient model fusion strategy for retinal disease classification using lightweight CNN architecture. Our method addresses key challenges such as over-fitting and limited dataset variability by training multiple models with distinct data augmentation strategies and different model complexities. through this fusion technique, we achieved robust generalization in data-scarce environments while balancing computational efficiency with feature extraction capabilities. As a result, we secured 3rd place in the MICCAI UWF4DR 2024 Challenge for image quality assessment in ultra-widefield fundus images. Our software package is available at https://***/Retinal-Research/NN-MOBILENET. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damage detection

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Frequency-Aware Inverse-Consistent Deep Learning for OCT-Angiogram Super-Resolution 25th

Frequency-Aware Inverse-Consistent Deep Learning for OCT-Ang...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Zhang, Weiwen Yang, Dawei Cheung, Carol Y. Chen, Hao Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Univ Hong Kong Dept Ophthalmol & Visual Sci Hong Kong Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Optical Coherence Tomography Angiography (OCTA) is a novel imaging modality that captures the retinal and choroidal microvasculature in a non-invasive way. So far, 3 mm x 3mm and 6 mm x 6 mm scanning protocols have been the two most widely-used field-of-views. Nevertheless, since both are acquired with the same number of A-scans, resolution of 6 mm x 6 mm image is inadequately sampled, compared with 3mm x 3mm Moreover, conventional supervised super-resolution methods for OCTA images are trained with pixel-wise registered data, while clinical data is mostly unpaired. this paper proposes an inverse-consistent generative adversarial network (GAN) for archiving 6 mm x 6 mm OCTA images with super-resolution. Our method is designed to be trained with unpaired 3 mm x 3mm and 6 mm x 6 mm OCTA image datasets. To further enhance the super-resolution performance, we introduce frequency transformations to refine high-frequency information while retaining low-frequency information. Compared with other state-of-the-art methods, our approach outperforms them on various performance metrics.

关键词： OCTA image Super-Resolution GAN Frequency-aware Inverse-consistency

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Semi-supervised Histological image Segmentation via Hierarchical Consistency Enforcement 25th

Semi-supervised Histological Image Segmentation via Hierarch...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Jin, Qiangguo Cui, Hui Sun, Changming Zheng, Jiangbin Wei, Leyi Fang, Zhenyu Meng, Zhaopeng Su, Ran Northwestern Polytech Univ Sch Software Xian Shaanxi Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Australia CSIRO Data61 Sydney NSW Australia Tianjin Univ Sch Comp Software Coll Intelligence & Comp Tianjin Peoples R China Tianjin Univ Tradit Chinese Med Tianjin Peoples R China Shandong Univ Sch Software Shandong Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Acquiring pixel-level annotations for histological image segmentation is time- and labor- consuming. Semi-supervised learning enables learning from the unlabeled and limited amount of labeled data. A challenging issue is the inconsistent and uncertain predictions on unlabeled data. To enforce invariant predictions over the perturbations applied to the hidden feature space, we propose a Mean-Teacher based hierarchical consistency enforcement (HCE) framework and a novel hierarchical consistency loss (HC-loss) with learnable and selfguided mechanisms. Specifically, the HCE takes the perturbed versions of the hierarchical features from the encoder as input to the auxiliary decoders, and encourages the predictions of the auxiliary decoders and the main decoder to be consistent. the HC-loss facilitates the teacher model to generate reliable guidance and enhances the consistency among all the decoders of the student model. the proposed method is simple, yet effective, which can easily be extended to other frameworks. the quantitative and qualitative experimental results indicate the effectiveness of the hierarchical consistency enforcement on the MoNuSeg and CRAG datasets.

关键词： Hierarchical consistency enforcement Semi-supervised learning Histological image segmentation

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Atlas-Based Semantic Segmentation of Prostate Zones 25th

Atlas-Based Semantic Segmentation of Prostate Zones

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Zhang, Jiazhen Venkataraman, Rajesh Staib, Lawrence H. Onofrey, John A. Yale Univ Dept Radiol & Biomed Imaging New Haven CT 06520 USA Yale Univ Dept Urol New Haven CT 06520 USA Yale Univ Dept Elect Engn New Haven CT USA Yale Univ Dept Biomed Engn New Haven CT 06520 USA Eigen Hlth Grass Valley CA USA

ISBN: (纸本)9783031164439;9783031164422

Segmentation of the prostate into specific anatomical zones is important for radiological assessment of prostate cancer in magnetic resonance imaging (MRI). Of particular interest is segmenting the prostate into two regions of interest: the central gland (CG) and peripheral zone (PZ). In this paper, we propose to integrate an anatomical atlas of prostate zone shape into a deep learning semantic segmentation framework to segment the CG and PZ in T2-weighted MRI. Our approach incorporates anatomical information in the form of a probabilistic prostate zone atlas and utilizes a dynamically controlled hyperparameter to combine the atlas with the semantic segmentation result. In addition to providing significantly improved segmentation performance, this hyperparameter is capable of being dynamically adjusted during the inference stage to provide users with a mechanism to refine the segmentation. We validate our approach using an external test dataset and demonstrate Dice similarity coefficient values (mean +/- SD) of 0.91 +/- 0.05 for the CG and 0.77 +/- 0.16 for the PZ that significantly improves upon the baseline segmentation results without the atlas. All code is publicly available on Github: https://***/OnofreyLab/prostate_ atlas_segm_miccai2022.

关键词： Deep learning image segmentation Probabilistic atlas Prostate MRI

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Weakly Supervised Segmentation by Tensor Graph Learning for Whole Slide images 25th

Weakly Supervised Segmentation by Tensor Graph Learning for ...

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Zhang, Qinghua Chen, Zhao Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Semantic segmentation of whole slide images (WSIs) helps pathologists identify lesions and cancerous nests. However, training fully supervised segmentation networks usually requires plenty of pixel-level annotations, which consume lots of time and human efforts. Coming from tissues of different patients with large amounts of pixels, WSIs exhibit various patterns, resulting in intra-class heterogeneity and inter-class homogeneity. Meanwhile, most existing methods for WSIs focus on extracting a certain type of features, neglecting the relations between different features and their joint effect on segmentation. therefore, we propose a novel weakly supervised network based on tensor graphs (WSNTG) for WSI segmentation. Using only sparse point annotations, it efficiently segments WSIs by superpixel-wise classification and credible node reweighting. To deal with the variability of WSIs, the proposed network represents multiple handcrafted features and hierarchical features yielded by a pretrained Convolutional Neural Network (CNN). Particularly, it learns over the semi-labeled tensor graphs constructed on the hierarchical features to exploit nonlinear data structures and associations. It gains robustness via the tensor-graph Laplacian of the hand-crafted features superimposed on the segmentation loss. We evaluated WSNTG on two WSI datasets, DigestPath2019 and SICAPV2. Results show that it outperforms many fully supervised and weakly supervised methods with minimal point annotations in WSI segmentation. the codes are published at hups://***/zqh369/WSNTG.

关键词： Weakly-supervised segmentation Pathology image segmentation Graph convolutional networks Node reweighting

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Triple-View Feature Learning for medical image Segmentation 1

引用

1st international Workshop on Resource-Efficient medical image Analysis (REMIA) / 25th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Wang, Ziyang Voiculescu, Irina Univ Oxford Dept Comp Sci Oxford England

ISBN: (数字)9783031168765

ISBN: (纸本)9783031168765;9783031168758

Deep learning models, e.g. supervised Encoder-Decoder style networks, exhibit promising performance in medical image segmentation, but come with a high labelling cost. We propose TriSegNet, a semi-supervised semantic segmentation framework. It uses triple-view feature learning on a limited amount of labelled data and a large amount of unlabeled data. the triple-view architecture consists of three pixel-level classifiers and a low-level shared-weight learning module. the model is first initialized with labelled data. Label processing, including data perturbation, confidence label voting and unconfident label detection for annotation, enables the model to train on labelled and unlabeled data simultaneously. the confidence of each model gets improved through the other two views of the feature learning. this process is repeated until each model reaches the same confidence level as its counterparts. this strategy enables triple-view learning of generic medical image datasets. Bespoke overlap-based and boundary-based loss functions are tailored to the different stages of the training. the segmentation results are evaluated on four publicly available benchmark datasets including Ultrasound, CT, MRI, and Histology images. Repeated experiments demonstrate the effectiveness of the proposed network compared against other semi-supervised algorithms, across a large set of evaluation measures.

关键词： Deep learning

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DOMINO: Domain-Aware Model Calibration in medical image Segmentation 25th

DOMINO: Domain-Aware Model Calibration in Medical Image Segm...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Stolte, Skylar E. Volle, Kyle Indahlastari, Aprinda Albizu, Alejandro Woods, Adam J. Brink, Kevin Hale, Matthew Fang, Ruogu Univ Florida UF J Crayton Pruitt Family Dept Biomed Engn Herbert Wertheim Coll Engn Gainesville FL 32611 USA UF Herbert Wertheim Coll Engn Dept Mech & Aerosp Engn Gainesville FL USA UF McKnight Brain Inst Ctr Cognit Aging & Memory Gainesville FL 32611 USA UF Coll Publ Hlth & Hlth Pro Dept Clin & Hlth Psychol Gainesville FL USA UF Coll Med Dept Neurosci Gainesville FL USA US Air Force Res Lab Eglin AFB FL USA UF Dept Elect & Comp Engn Herbert Wertheim Coll Engn Gainesville FL 32611 USA

ISBN: (纸本)9783031164439;9783031164422

Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applications. Unfortunately, modern deep neural networks are poorly calibrated, compromising trustworthiness and reliability. medical image segmentation particularly suffers from this due to the natural uncertainty of tissue boundaries. this is exasperated by their loss functions, which favor overconfidence in the majority classes. We address these challenges with DOMINO, a domain-aware model calibration method that leverages the semantic confusability and hierarchical similarity between class labels. Our experiments demonstrate that our DOMINO-calibrated deep neural networks outperform non-calibrated models and state-of-the-art morphometric methods in head image segmentation. Our results show that our method can consistently achieve better calibration, higher accuracy, and faster inference times than these methods, especially on rarer classes. this performance is attributed to our domain-aware regularization to inform semantic model calibration. these findings show the importance of semantic ties between class labels in building confidence in deep learning models. the framework has the potential to improve the trustworthiness and reliability of generic medical image segmentation models.

关键词： image segmentation Machine learning uncertainty Model calibration Model generalizability Whole head MRI

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AutoLaparo: A New Dataset of Integrated Multi-tasks for image-guided Surgical Automation in Laparoscopic Hysterectomy 25th

AutoLaparo: A New Dataset of Integrated Multi-tasks for Imag...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Wang, Ziyi Lu, Bo Long, Yonghao Zhong, Fangxun Cheung, Tak-Hong Dou, Qi Liu, Yunhui Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Chinese Univ Hong Kong T Stone Robot Inst Hong Kong Peoples R China Soochow Univ Sch Mech & Elect Engn Robot & Microsyst Ctr Suzhou Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Univ Hong Kong Prince Wales Hosp Dept Obstet & Gynaecol Hong Kong Peoples R China

ISBN: (纸本)9783031164491;9783031164484

computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. the video data streamed from the endoscope provides rich information to support context-awareness for next-generation intelligent surgical systems. To achieve accurate perception and automatic manipulation during the procedure, learning based technique is a promising way, which enables advanced image analysis and scene understanding in recent years. However, learning such models highly relies on large-scale, high-quality, and multi-task labelled data. this is currently a bottleneck for the topic, as available public dataset is still extremely limited in the field of CAI. In this paper, we present and release the first integrated dataset (named AutoLaparo) with multiple image-based perception tasks to facilitate learning-based automation in hysterectomy surgery. Our AutoLaparo dataset is developed based on full-length videos of entire hysterectomy procedures. Specifically, three different yet highly correlated tasks are formulated in the dataset, including surgical workflow recognition, laparoscope motion prediction, and instrument and key anatomy segmentation. In addition, we provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset. the dataset is available at https://***.

关键词： Surgical video dataset image-guided robotic surgical automation Laparoscopic hysterectomy

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