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检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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Self-supervised Multi-modality image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer 2nd

Self-supervised Multi-modality Image Feature Extraction for ...

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on medical image computing and computer-assisted intervention, MICCAI 2021

作者： Ma, Baoqiang Guo, Jiapan De Biase, Alessia Sourlos, Nikos Tang, Wei van Ooijen, Peter Both, Stefan Sijtsema, Nanna Maria RB Groningen9700 Netherlands

ISBN: (纸本)9783030982522

Long-term survival of oropharyngeal squamous cell carcinoma patients (OPSCC) is quite poor. Accurate prediction of Progression Free Survival (PFS) before treatment could make identification of high-risk patients before treatment feasible which makes it possible to intensify or de-intensify treatments for high- or low-risk patients. In this work, we proposed a deep learning based pipeline for PFS prediction. the proposed pipeline consists of three parts. Firstly, we utilize the pyramid autoencoder for image feature extraction from both CT and PET scans. Secondly, the feed forward feature selection method is used to remove the redundant features from the extracted image features as well as clinical features. Finally, we feed all selected features to a DeepSurv model for survival analysis that outputs the risk score on PFS on individual patients. the whole pipeline was trained on 224 OPSCC patients. We have achieved a average C-index of 0.7806 and 0.7967 on the independent validation set for task 2 and task 3. the C-indices achieved on the test set are 0.6445 and 0.6373, respectively. It is demonstrated that our proposed approach has the potential for PFS prediction and possibly for other survival endpoints. © 2022, Springer Nature Switzerland AG.

关键词： Pipelines

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Detection of the Crown of thorns starfish using YOLOv5

Detection of the Crown of Thorns starfish using YOLOv5

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Pankaj Kunekar Pradnya Vishal Patil Babeetta Bbhagat Tejal H. Patil Neha Rajas Pratiksha Yuvraj Sawant Department of Information Technology Vishwakarma Institute of Technology Pune Maharashtra India Computer Engineering Pillai HOC college of engineering Rasayani MIT School of Computing Pune Maharashtra India Department of AI&DS Sinhgad College of Engineering Pune Maharashtra India Department of AI&DS Vishwakarma Institute of Technology Pune Maharashtra India Department of Computer Science PVG’s College of Science and Commerce Pune Maharashtra India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

the invasive Crown-of-thorns starfish (COTS) poses a significant threat to coral reef ecosystems. Early detection and removal are crucial to mitigating their impact. this research proposes a YOLOv5-based object detection system to accurately identify and locate COTS in underwater images. By fine-tuning the YOLOv5 model with a dataset of COTS images, the system can effectively detect and classify COTS with high precision and recall. the output of the system includes bounding boxes and confidence scores for each detected COTS, enabling timely intervention and removal efforts. this research contributes to the conservation of coral reefs by providing a valuable tool for monitoring and controlling COTS populations. the proposed system can be integrated into underwater drones or diveroperated devices to facilitate efficient and accurate COTS detection and removal.

关键词： YOLO Accuracy Ecosystems Marine vegetation Network architecture Object recognition image preprocessing Optimization Monitoring image classification

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Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer 1

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作者： Salmanpour, Mohammad R. Hajianfar, Ghasem Rezaeijo, Seyed Masoud Ghaemi, Mohammad Rahmim, Arman University of British Columbia VancouverBC Canada BC Cancer Research Institute VancouverBC Canada VancouverBC Canada Rajaie Cardiovascular Medical and Research Center Iran University of Medical Science Tehran Iran Department of Medical Physics School of Medicine Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran Kerman University of Medical Sciences Tehran Iran

ISBN: (数字)9783030982539

ISBN: (纸本)9783030982522

In this study, 325 subjects were extracted from the HECKTOR-Challenge. 224 subjects were considered in the training procedure, and 101 subjects were employed to validate our models. Positron emission tomography (PET) images were registered to computed tomography (CT) images, enhanced, and cropped. First, 10 fusion techniques were utilized to combine PET and CT information. We also utilized 3D-UNETR (UNET with Transformers) and 3D-UNET to automatically segment head and neck squamous cell carcinoma (HNSCC) tumors and then extracted 215 radiomics features from each region of interest via our Standardized Environment for Radiomics Analysis (SERA) radiomics package. Subsequently, we employed multiple hybrid machine learning systems) HMLS), including 13 dimensionality reduction algorithms and 15 feature selection algorithms linked with 8 survival prediction algorithms, optimized by 5-fold cross-validation, applied to PET only, CT only and 10 fused datasets. We also employed Ensemble Voting for the prediction task. Test dice scores and test c-indices were reported to compare models. For segmentation, the highest dice score of 0.680 was achieved by the Laplacian-pyramid fusion technique linked with 3D-UNET. the highest c-index of 0.680 was obtained via the Ensemble Voting technique for survival prediction. We demonstrated that employing fusion techniques followed by appropriate automatic segmentation technique results in a good performance. Moreover, utilizing the Ensemble Voting technique enabled us to achieve the highest performance. © 2022, Springer Nature Switzerland AG.

关键词： image enhancement

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Deep Convolution Neural Network-Based Classification and Diagnosis of Heart Disease using ElectroCardioGram (ECG) images

Deep Convolution Neural Network-Based Classification and Dia...

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international conference for Convergence of Technology (I2CT)

作者： thanu Kurian thangam. S Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

A cardiovascular disease, if identified correctly at an early stage, could reduce the critical consequences in patients , including fatality. One of the best diagnostic tool for detecting heart disease is through an ECG test. Models trained using signal data related to ECG is difficult to be implemented in an actual healthcare scenario. A CNN model is proposed which makes use of 12-lead ECG images to diagnose cardiac conditions such as myocardial infarction, abnormal heart beat, history of myocardial infarction and normal heartbeat. the ECG image can be taken by scanning the image using a smart phone. this would be very helpful in small healthcare centers where there are no experts for diagnosis. the proposed model was efficiently trained with an accuracy of 99% and cardiac condition was diagnosed using ECG images scanned using a mobile with a superior performance. the work also compares the performance of model with pretrained models as ResNet and EfficientNet-B0 for the same ECG image dataset.

关键词： Heart Heart beat Convolution medical services Electrocardiography Myocardium Data models

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FAST: a dual-tier few-shot learning paradigm for whole slide image classification 24

FAST: a dual-tier few-shot learning paradigm for whole slide...

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Proceedings of the 38th international conference on Neural Information Processing Systems

作者： Kexue Fu Xiaoyuan Luo Linhao Qu Shuo Wang Ying Xiong Ilias Maglogiannis Longxiang Gao Manning Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Fudan University University of Piraeus

ISBN: (纸本)9798331314385

the expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. they lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST.

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Efficient Semi-supervised Tooth Instance Segmentation in Panoramic X-Rays Using ResUnet50 and SAM Networks

Efficient Semi-supervised Tooth Instance Segmentation in Pa...

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2nd international ToothFairy2: Multi-Structure Segmentation in CBCT Volumes, ToothFairy 2024, 3D Teeth Landmarks Detection Challenge, 3DTeethLand 2024, Semi-supervised Teeth Segmentation, STS 2024 held in conjunction with the 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Guo, Xinge Liu, Wenxi Cui, Zihao Department of Biomedical Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783031889769

In this paper, we propose a semi-supervised approach using a ResUnet50-based deep learning model for tooth instance segmentation in 2D panoramic X-ray images. Unlabeled data was leveraged through pseudo-label generation to enhance model performance. Our method was evaluated on the STS 2024 Challenge dataset, achieving an average Dice score of 79.15% at the image-level and 45.58% at the instance-level. Additionally, it demonstrated competitive performance with IoU scores of 83.90% and 39.29%, respectively. the model efficiently segmented individual teeth, although challenges remain in cases with implants and other high-density artifacts. the average running time for processing each image was 10 s, and the model was trained using an NVIDIA Tesla V100-SXM2 GPU. Despite these promising results, future work will focus on improving instance-level segmentation accuracy, particularly in challenging cases. the code is available at http://***/Guo777777. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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4th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021 held in Conjunction with 24th international conference on medical Imaging computing and computer-assisted intervention, MICCAI 2021

4th International Workshop on Machine Learning in Clinical N...

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4th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021 held in Conjunction with 24th international conference on medical Imaging computing and computer-assisted intervention, MICCAI 2021

ISBN: (纸本)9783030875855

the proceedings contain 17 papers. the special focus in this conference is on Machine Learning in Clinical Neuroimaging. the topics include: Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation;Deep Stacking Networks for Conditional Nonlinear Granger Causal Modeling of fMRI Data;Dynamic Adaptive Spatio-Temporal Graph Convolution for fMRI Modelling;structure-Function Mapping via Graph Neural Networks;improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity;h3K27M Mutations Prediction for Brainstem Gliomas Based on Diffusion Radiomics Learning;Constrained Learning of Task-Related and Spatially-Coherent Dictionaries from Task fMRI Data;distinguishing Healthy Ageing from Dementia: A Biomechanical Simulation of Brain Atrophy Using Deep Networks;towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows;Patch vs. Global image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients;MRI image Registration Considerably Improves CNN-Based Disease Classification;dynamic Sub-graph Learning for Patch-Based Cortical Folding Classification;detection of Abnormal Folding Patterns with Unsupervised Deep Generative Models;PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction;multi-modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network.

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Video Foundation Model for medical 3D Segmentation 2nd

Video Foundation Model for Medical 3D Segmentation

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作者： Ma, Qi Sun, Guolei Tombak, Güney Ik Jain, Shipra Huber, Niko Benjamin Gool, Luc Van Konukoglu, Ender Computer Vision Laboratory ETH Zurich Zurich Switzerland INSAIT Sofia University Sofia Bulgaria Align Technology San Jose United States

ISBN: (纸本)9783031889769

Video foundation models are gaining significant attention due to their ability to deliver highly accurate and efficient performance when pretrained on large-scale natural video datasets, especially compared to models trained from scratch. these pretrained models offer a substantial advantage in extracting rich features, enabling faster fine-tuning and more accurate downstream tasks. In the medical field, growing research has shown that treating 3D volumes as video sequences is effective. However, the models trained on limited 3D medical data extract unsatisfactory features due to insufficient training, leading to performance loss. therefore, we adapt video foundation models pretrained on large-scale RGB videos for segmenting 3D CT volumes, to make use of their ability of extracting high-quality features. Our method is evaluated on the ToothFairy2 and AMOS datasets, where it outperforms other transformer-based methods, bridging the gap between video foundation models and 3D medical segmentation. Our results demonstrate that knowledge from video foundation models trained on large-scale RGB datasets can be effectively transferred to medical segmentation tasks, achieving impressive performance even without training on any medical dataset. Additionally, we conduct extensive experiments exploring various design choices for the encoder, decoder, and domain adaptation mechanisms, offering comprehensive insights into adapting video foundation models for 3D medical segmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Controllable Cardiac Synthesis via Disentangled Anatomy Arithmetic 24th

Controllable Cardiac Synthesis via Disentangled Anatomy Arit...

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

作者： thermos, Spyridon Liu, Xiao O'Neil, Alison Tsaftaris, Sotirios A. Univ Edinburgh Sch Engn Edinburgh EH9 3FB Midlothian Scotland Alan Turing Inst London NW1 2DB England Canon Med Res Europe Edinburgh EH6 5NP Midlothian Scotland

ISBN: (纸本)9783030871994;9783030871987

Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can correct such under-representation. Assuming a proper latent representation, the idea of a "latent vector arithmetic" could offer the means of achieving such synthesis. A proper representation must encode the fidelity of the input data, preserve invariance and equivariance, and permit arithmetic operations. Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (vector) representations, we propose a framework termed "disentangled anatomy arithmetic" , in which a generative model learns to combine anatomical factors of different input images such that when they are re-entangled with the desired imaging modality (e.g. MRI), plausible new cardiac images are created with the target characteristics. To encourage a realistic combination of anatomy factors after the arithmetic step, we propose a localized noise injection network that precedes the generator. Our model is used to generate realistic images, pathology labels, and segmentation masks that are used to augment the existing datasets and subsequently improve post-hoc classification and segmentation tasks. Code is publicly available at https://***/vios-s/DAA-GAN.

关键词： Disentangled anatomy arithmetic Semantic image synthesis Cardiac data augmentation

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Deformable Inherent Consistent Learning Network for Accurate Tooth Segmentation in Dental Panoramic Radiographs 2nd

Deformable Inherent Consistent Learning Network for Accurat...

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作者： Cai, Xinxu Zhang, Yisong Guan, Zeyuan Sun, Qi Qu, Zhenshen Harbin Institute of Technology Harbin China

ISBN: (纸本)9783031889769

this study presents a Deformable Inherent Consistent Learning (DICL) network for tooth segmentation in dental panoramic radiographs, addressing the clinical need for accurate diagnostic tools amid the complexity of dental diseases. the DICL network enhances segmentation accuracy by effectively learning from a limited labeled dataset, guiding robust categorical representation of teeth. It employs deformable convolution to capture detailed dental features and a two-stage training strategy, transitioning from semi-supervised to fully supervised learning, optimizing performance with labeled and pseudo-labeled images. Achieving scores of 84.45% (image-level Dice), 73.60% (image-level IoU), 88.57% (image-level NSD), 22.36% (instance-level Dice), 57.39% (instance-level IoU), 69.85% (instance-level NSD) and 65.82% IA on the official test set, our method demonstrates superior segmentation effectiveness, especially in boundary and root segmentation, outperforming algorithms like U-Net. this study’s contributions advance dental image segmentation, improving diagnostic efficiency and reducing errors. the code is available at https://***/Dew026/***. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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