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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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FedGS: Federated Gradient Scaling for Heterogeneous medical image Segmentation 9th

FedGS: Federated Gradient Scaling for Heterogeneous Medical ...

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27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Schutte, Philip Corbetta, Valentina Beets-Tan, Regina Silva, Wilson Univ Amsterdam Amsterdam Netherlands Netherlands Canc Inst Dept Radiol Amsterdam Netherlands Univ Utrecht Dept Informat & Comp Sci Dept Biol AI Technol Life Utrecht Netherlands Maastricht Univ GROW Sch Oncol & Dev Biol Med Ctr Maastricht Netherlands

ISBN: (纸本)9783031776090;9783031776106

Federated Learning (FL) in Deep Learning (DL)-automated medical image segmentation helps preserving privacy by enabling collaborative model training without sharing patient data. However, FL faces challenges with data heterogeneity among institutions, leading to suboptimal global models. Integrating Disentangled Representation Learning (DRL) in FL can enhance robustness by separating data into distinct representations. Existing DRL methods assume heterogeneity lies solely in style features, overlooking content-based variability like lesion size and shape. We propose FedGS, a novel FL aggregation method, to improve segmentation performance on small, under-represented targets while maintaining overall efficacy. FedGS demonstrates superior performance over FedAvg, particularly for small lesions, across PolypGen and LiTS datasets. the code and pre-trained checkpoints are available at the following link: https://***/Trustworthy-AI-UU-NKI/Federated-Learning-Disentanglement.

关键词： Federated learning

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A General Computationally-Efficient 3D Reconstruction Pipeline for Multiple images with Point Clouds 26th

A General Computationally-Efficient 3D Reconstruction Pipeli...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Wu, Qingyang Shen, Yiqing Ke, Jing Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Johns Hopkins Univ Dept Comp Sci Baltimore MD USA Univ New South Wales Sch Comp Sci & Engn Sydney Australia

ISBN: (纸本)9783031474002;9783031474019

Histology images are the golden standard for medical diagnostic analysis. However, 2D images can lose some critical information, such as the spatial structure of blood vessels. therefore, it is necessary to perform 3D reconstruction for the histology images. At the same time, due to the differences between institutions and hospitals, a general 3D reconstruction method is needed. In this work, we propose a 3D reconstruction pipeline that is compatible with Whole Slide Imaging (WSI) and can also be applied to other imaging modalities such as CT images, MRI images, and immunohistochemistry images. through semantic segmentation, point cloud construction and registration, and 3D rendering, we can reconstruct serialized images into 3D models. By optimizing the pipeline workflow, we can significantly reduce the computation workload required for the 3D reconstruction of high-resolution images and thus save time. In clinical practice, our method helps pathologists triage and evaluate tumor tissues with real-time 3D visualization.

关键词： Histology image Point Clouds 3D Reconstruction

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Annotator Consensus Prediction for medical image Segmentation with Diffusion Models 26th

Annotator Consensus Prediction for Medical Image Segmentatio...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Amit, Tomer Shichrur, Shmuel Shaharabany, Tal Wolf, Lior Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9783031439001;9783031439018

A major challenge in the segmentation of medical images is the large inter- and intra-observer variability in annotations provided by multiple experts. To address this challenge, we propose a novel method for multi-expert prediction using diffusion models. Our method leverages the diffusion-based approach to incorporate information from multiple annotations and fuse it into a unified segmentation map that reflects the consensus of multiple experts. We evaluate the performance of our method on several datasets of medical segmentation annotated by multiple experts and compare it with the state-of-the-art methods. Our results demonstrate the effectiveness and robustness of the proposed method. Our code is publicly available at https://***/tomeramit/Annotator-Consensus-Prediction.

关键词： Multi annotator image segmentation Diffusion Model

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MIM-OOD: Generative Masked image Modelling for Out-of-Distribution Detection in medical images 3rd

MIM-OOD: Generative Masked Image Modelling for Out-of-Distri...

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3rd Workshop on Deep Generative Models for medical image computing and computer assisted intervention (DGM4MICCAI) at the 26th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Marimont, Sergio Naval Siomos, Vasilis Tarroni, Giacomo City Univ London CitAI Res Ctr London England Imperial Coll London BioMedIA London England

ISBN: (纸本)9783031537660;9783031537677

Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images leveraging only models trained on images of healthy anatomy. An established approach is to tokenize images and model the distribution of tokens with Auto-Regressive (AR) models. AR models are used to 1) identify anomalous tokens and 2) inpaint anomalous representations with in-distribution tokens. However, AR models are slow at inference time and prone to error accumulation issues which negatively affect OOD detection performance. Our novel method, MIM-OOD, overcomes both speed and error accumulation issues by replacing the AR model with two task-specific networks: 1) a transformer optimized to identify anomalous tokens and 2) a transformer optimized to in-paint anomalous tokens using masked image modelling (MIM). Our experiments with brain MRI anomalies show that MIM-OOD substantially outperforms AR models (DICE 0.458 vs 0.301) while achieving a nearly 25x speedup (9.5 s vs 244 s).

关键词： out-of-distribution detection unsupervised learning masked image modelling

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Memory Replay for Continual medical image Segmentation through Atypical Sample Selection 26th

Memory Replay for Continual Medical Image Segmentation Throu...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Bera, Sutanu Ummadi, Vinay Sen, Debashis Mandal, Subhamoy Biswas, Prabir Kumar Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9783031439001;9783031439018

medical image segmentation is critical for accurate diagnosis, treatment planning and disease monitoring. Existing deep learning-based segmentation models can suffer from catastrophic forgetting, especially when faced with varying patient populations and imaging protocols. Continual learning (CL) addresses this challenge by enabling the model to learn continuously from a stream of incoming data without the need to retrain from scratch. In this work, we propose a continual learning-based approach for medical image segmentation using a novel memory replay-based learning scheme. the approach uses a simple and effective algorithm for image selection to create the memory bank by ranking and selecting images based on their contribution to the learning process. We evaluate our proposed algorithm on three different problems and compare it with several baselines, showing significant improvements in performance. Our study highlights the potential of continual learning-based algorithms for medical image segmentation and underscores the importance of efficient sample selection in creating memory banks.

关键词： Memory Replay Continual Learning medical image Segmentation

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Continual-GEN: Continual Group Ensembling for Domain-agnostic Skin Lesion Classification 26th

Continual-GEN: Continual Group Ensembling for Domain-agnosti...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Bayasi, Nourhan Du, Siyi Hamarneh, Ghassan Garbi, Rafeef Univ British Columbia BiSICL Vancouver BC Canada Simon Fraser Univ Med Image Anal Lab Burnaby BC Canada

ISBN: (纸本)9783031474002;9783031474019

Designing deep learning (DL) models that adapt to new data without forgetting previously acquired knowledge is important in the medical field where data is generated daily, posing a challenge for model adaptation and knowledge retention. Continual learning (CL) enables models to learn continuously without forgetting, typically on a sequence of domains with known domain identities (e.g. source of data). In this work, we address a more challenging and practical CL scenario where information about the domain identity during training and inference is unavailable. We propose Continual-GEN, a novel forget-free, replay-free, and domain-agnostic subnetwork-based CL method for medical imaging with a focus on skin lesion classification. Continual-GEN proposes an ensemble of groups approach that decomposes the training data for each domain into groups of semantically similar clusters. Given two domains, Continual-GEN assesses the similarity between them based on the distance between their ensembles and assigns a separate subnetwork if the similarity score is low, otherwise updating the same subnetwork to learn both domains. At inference, Continual-GEN selects the best subnetwork using a distance-based metric for each test data, which is directly used for classification. Our quantitative experiments on four skin lesion datasets demonstrate the superiority of Continual-GEN over state-of-the-art CL methods, highlighting its potential for practical applications in medical imaging. Our code: https://***/nourhanb/Continual-GEN.

关键词： Continual Learning Domain-agnostic Out-of-Distribution Detection Skin Lesion Classification Dermatology

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4th international Workshop on Patch-Based Techniques in medical Imaging, Patch-MI 2018 Held in Conjunction with 21st international conference on medical image computing and computer-assisted intervention, MICCAI 2018

4th International Workshop on Patch-Based Techniques in Medi...

引用

4th international Workshop on Patch-Based Techniques in medical Imaging, Patch-MI 2018 Held in Conjunction with 21st international conference on medical image computing and computer-assisted intervention, MICCAI 2018

ISBN: (纸本)9783030004996

the proceedings contain 15 papers. the special focus in this conference is on Patch-Based Techniques in medical Imaging. the topics include: LesionBrain: An online tool for white matter lesion segmentation;multi-atlas parcellation in the presence of lesions: Application to multiple sclerosis;a patch-based segmentation approach with high level representation of the data for cortical sulci recognition;tumor delineation for brain radiosurgery by a ConvNet and non-uniform patch generation;iterative deep retinal topology extraction;MRI denoising using deep learning;MRI denoising and artefact removal using self-organizing maps for fast global block-matching;a Monte Carlo framework for denoising and missing wedge reconstruction in cryo-electron tomography;robust supervoxel matching combining mid-level spectral and context-rich features;stereo matching for wireless capsule endoscopy using direct attenuation model;liver tissue classification using an auto-context-based deep neural network with a multi-phase training framework;using 1D patch-based signatures for efficient cascaded classification of lung nodules;predicting future bone infiltration patterns in multiple myeloma.

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FedAutoMRI: Federated Neural Architecture Search for MR image Reconstruction 26th

FedAutoMRI: Federated Neural Architecture Search for MR Imag...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Wu, Ruoyou Li, Cheng Zou, Juan Wang, Shanshan Chinese Acad Sci Shenzhen Inst Adv Technol Paul C Lauterbur Res Ctr Biomed Imaging Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Xiangtan Univ Sch Phys & Optoelect Xiangtan 411105 Peoples R China

ISBN: (纸本)9783031474002;9783031474019

Centralized training methods have shown promising results in MR image reconstruction, but privacy concerns arise when gathering data from multiple institutions. Federated learning, a distributed collaborative training scheme, can utilize multi-center data without the need to transfer data between institutions. However, existing federated learning MR image reconstruction methods rely on manually designed models which have extensive parameters and suffer from performance degradation when facing heterogeneous data distributions. To this end, this paper proposes a novel FederAted neUral archiTecture search approach fOr MR image reconstruction (FedAutoMRI). the proposed method utilizes differentiable architecture search to automatically find the optimal network architecture. In addition, an exponential moving average method is introduced to improve the robustness of the client model to address the data heterogeneity issue. To the best of our knowledge, this is the first work to use federated neural architecture search for MR image reconstruction. Experimental results demonstrate that our proposed FedAutoMRI can achieve promising performances while utilizing a lightweight model with only a small number of model parameters compared to the classical federated learning methods.

关键词： Magnetic resonance imaging (MRI) Federated learning Neural architecture search

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Ariadne's thread: Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images 26th

Ariadne's Thread: Using Text Prompts to Improve Segmentation...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Zhong, Yi Xu, Mengqiu Liang, Kongming Chen, Kaixin Wu, Ming Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections. Existing medical image segmentation methods are almost uni-modal methods based on image. However, these image-only methods tend to produce inaccurate results unless trained with large amounts of annotated data. To overcome this challenge, we propose a language-driven segmentation method that uses text prompt to improve to the segmentation result. Experiments on the QaTa-COV19 dataset indicate that our method improves the Dice score by 6.09% at least compared to the uni-modal methods. Besides, our extended study reveals the flexibility of multi-modal methods in terms of the information granularity of text and demonstrates that multi-modal methods have a significant advantage over image-only methods in terms of the size of training data required.

关键词： Multi-modal Learning medical image Segmentation

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Ultrasound image Reconstruction with Denoising Diffusion Restoration Models 3rd

Ultrasound Image Reconstruction with Denoising Diffusion Res...

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作者： Zhang, Yuxin Huneau, Clement Idier, Jerome Mateus, Diana Nantes Univ Ecole Cent Nantes LS2N CNRSUMR 6004 F-44000 Nantes France

ISBN: (纸本)9783031537660;9783031537677

Ultrasound image reconstruction can be approximately cast as a linear inverse problem that has traditionally been solved with penalized optimization using the l(1) or l(2) norm, or wavelet-based terms. However, such regularization functions often struggle to balance the sparsity and the smoothness. A promising alternative is using learned priors to make the prior knowledge closer to reality. In this paper, we rely on learned priors under the framework of Denoising Diffusion Restoration Models (DDRM), initially conceived for restoration tasks with natural images. We propose and test two adaptions of DDRM to ultrasound inverse problem models, DRUS and WDRUS. Our experiments on synthetic and PICMUS data show that from a single plane wave our method can achieve image quality comparable to or better than DAS and state-of-the-art methods. the code is available at https://***/Yuxin-Zhang-Jasmine/DRUS-v1/.

关键词： Ultrasound imaging Inverse Problems Diffusion models

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