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Fourier Test-Time Adaptation with Multi-level Consistency for Robust Classification 1

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

作者： Huang, Yuhao Yang, Xin Huang, Xiaoqiong Zhou, Xinrui Chi, Haozhe Dou, Haoran Hu, Xindi Wang, Jian Deng, Xuedong Ni, Dong Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Zhejiang Univ ZJU UIUC Inst Hangzhou Peoples R China Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed Leeds W Yorkshire England Shenzhen RayShape Med Technol Co Ltd Shenzhen Peoples R China Nanjing Med Univ Sch Biomed Engn & Informat Nanjing Peoples R China Nanjing Med Univ Affiliated Suzhou Hosp Suzhou Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Deep classifiers may encounter significant performance degradation when processing unseen testing data from varying centers, vendors, and protocols. Ensuring the robustness of deep models against these domain shifts is crucial for their widespread clinical application. In this study, we propose a novel approach called Fourier Test-time Adaptation (FTTA), which employs a dual-adaptation design to integrate input and model tuning, thereby jointly improving the model robustness. the main idea of FTTA is to build a reliable multi-level consistency measurement of paired inputs for achieving self-correction of prediction. Our contribution is two-fold. First, we encourage consistency in global features and local attention maps between the two transformed images of the same input. Here, the transformation refers to Fourier-based input adaptation, which can transfer one unseen image into source style to reduce the domain gap. Furthermore, we leverage style-interpolated images to enhance the global and local features with learnable parameters, which can smooth the consistency measurement and accelerate convergence. Second, we introduce a regularization technique that utilizes style interpolation consistency in the frequency space to encourage self-consistency in the logit space of the model output. this regularization provides strong self-supervised signals for robustness enhancement. FTTA was extensively validated on three large classification datasets with different modalities and organs. Experimental results show that FTTA is general and outperforms other strong state-of-the-art methods.

关键词： Classifier robustness Testing-time adaptation Consistency

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SMESwin Unet: Merging CNN and Transformer for medical image Segmentation 25th

SMESwin Unet: Merging CNN and Transformer for Medical Image ...

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

作者： Wang, Ziheng Min, Xiongkuo Shi, Fangyu Jin, Ruinian Nawrin, Saida S. Yu, Ichen Nagatomi, Ryoichi Tohoku Univ Grad Sch Biomed Engn Div Biomed Engn Hlth & Welf Sendai Japan Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Tohoku Univ Grad Sch Med Dept Med & Sci Sports & Exercise Sendai Japan

ISBN: (纸本)9783031164439;9783031164422

Vision transformer is the new favorite paradigm in medical image segmentation since last year, which surpassed the traditional CNN counterparts in quantitative metrics. the significant advantage of ViTs is to utilize the attention layers to model global relations between tokens. However, the increased representation capacity of ViTs comes with corresponding shortcomings: short of CNN's inductive biases (locality), translation invariance, and hierarchical structure of visual information. Consequently, well-trained ViTs require more data than CNNs. As high quality data in medical imaging area is always limited, we propose SMESwin UNet. Firstly, based on Channel-wise Cross fusion Transformer (CCT) we fuse multi-scale semantic features and attention maps by designing a compound structure with CNN and ViTs (named MCCT). Secondly, we introduce superpixel by dividing the pixel-level feature into district-level to avoid the interference of meaningless parts of the image. Finally, we used External Attention to consider the correlations among all data samples, which may further reduce the limitation of small datasets. According to our experiments, the proposed superpixel and MCCT-based Swin Unet (SMESwin Unet) achieves better performance than CNNs and other Transformer-based architectures on three medical image segmentation datasets (nucleus, cells, and glands).

关键词： Superpixels

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Semi-supervised medical image Segmentation Using Cross-Model Pseudo-Supervision with Shape Awareness and Local Context Constraints 25th

Semi-supervised Medical Image Segmentation Using Cross-Model...

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

作者： Liu, Jinhua Desrosiers, Christian Zhou, Yuanfeng Shandong Univ Sch Software Jinan Peoples R China Ecole Technol Super Software & IT Engn Dept Montreal PQ Canada

ISBN: (纸本)9783031164521;9783031164514

In semi-supervised medical image segmentation, the limited amount of labeled data available for training is often insufficient to learn the variability and complexity of target regions. To overcome these challenges, we propose a novel framework based on cross-model pseudo-supervision that generates anatomically plausible predictions using shape awareness and local context constraints. Our framework consists of two parallel networks, a shape-aware network and a shape-agnostic network, which provide pseudo-labels to each other for using unlabeled data effectively. the shape-aware network implicitly captures information on the shape of target regions by adding the prediction of the other network as input. On the other hand, the shape-agnostic network leverages Monte-Carlo dropout uncertainty estimation to generate reliable pseudo-labels to the other network. the proposed framework also comprises a new loss function that enables the network to learn the local context of the segmentation, thus improving the overall segmentation accuracy. Experiments on two publicly-available datasets show that our method outperforms state-of-the-art approaches for semi-supervised segmentation and better preserves anatomical morphology compared to these approaches. Code is available at https://***/igip-liu/SLC-Net.

关键词： Semi-supervised segmentation Shape awareness Cross-model pseudo-supervision Local context loss

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GSMorph: Gradient Surgery for Cine-MRI Cardiac Deformable Registration 26th

GSMorph: Gradient Surgery for Cine-MRI Cardiac Deformable Re...

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

作者： Dou, Haoran Bi, Ning Han, Luyi Huang, Yuhao Mann, Ritse Yang, Xin Ni, Dong Ravikumar, Nishant Frangi, Alejandro F. Huang, Yunzhi Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed CIS Leeds W Yorkshire England Radboud Univ Nijmegen Dept Radiol & Nucl Med Med Ctr Nijmegen Netherlands Netherlands Canc Inst Dept Radiol Amsterdam Netherlands Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Shenzhen RayShape Med Technol Co Ltd Shenzhen Peoples R China Univ Manchester Sch Comp Sci & Hlth Sci Div Informat Imaging & Data Sci Manchester Lancs England Katholieke Univ Leuven Med Imaging Res Ctr MIRC Elect Engn Dept Leuven Belgium Katholieke Univ Leuven Med Imaging Res Ctr MIRC Cardiovasc Sci Dept Leuven Belgium Alan Turing Inst London England Nanjing Univ Informat Sci & Technol Inst Med Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the deformation field. therefore, they inevitably require tuning the hyperparameter for optimal registration performance. Tuning the hyperparameters is highly computationally expensive and introduces undesired dependencies on domain knowledge. In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses. In GSMorph, we reformulate the optimization procedure by projecting the gradient of similarity loss orthogonally to the plane associated with the smoothness constraint, rather than additionally introducing a hyperparameter to balance these two competing terms. Furthermore, our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference. In this study, We compared our method with state-of-the-art (SOTA) deformable registration approaches over two publicly available cardiac MRI datasets. GSMorph proves superior to five SOTA learning-based registration models and two conventional registration techniques, SyN and Demons, on both registration accuracy and smoothness.

关键词： medical image registration Gradient surgery Regularization

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S²ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-Supervised Polyp Segmentation 26th

S<SUP>2</SUP>ME: Spatial-Spectral Mutual Teaching and Ensemb...

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

作者： Wang, An Xu, Mengya Zhang, Yang Islam, Mobarakol Ren, Hongliang Chinese Univ Hong Kong Shun Hing Inst Adv Engn SHIAE Dept Elect Engn Hong Kong Peoples R China Natl Univ Singapore Dept Biomed Engn Singapore Singapore Hubei Univ Technol Sch Mech Engn Wuhan Peoples R China UCL Dept Med Phys & Biomed Engn Wellcome EPSRC Ctr Intervent & Surg Sci WEISS London England

ISBN: (纸本)9783031439063;9783031439070

Fully-supervised polyp segmentation has accomplished significant triumphs over the years in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions from weak supervision like scribbles are rarely explored yet primarily meaningful and demanding in medical practice due to the expensiveness and scarcity of densely-annotated polyp data. Besides, various deployment issues, including data shifts and corruption, put forward further requests for model generalization and robustness. To address these concerns, we design a framework of Spatial-Spectral Dual-branch Mutual Teaching and Entropy-guided Pseudo Label Ensemble Learning (S2ME). Concretely, for the first time in weakly-supervised medical image segmentation, we promote the dual-branch co-teaching framework by leveraging the intrinsic complementarity of features extracted from the spatial and spectral domains and encouraging cross-space consistency through collaborative optimization. Furthermore, to produce reliable mixed pseudo labels, which enhance the effectiveness of ensemble learning, we introduce a novel adaptive pixelwise fusion technique based on the entropy guidance from the spatial and spectral branches. Our strategy efficiently mitigates the deleterious effects of uncertainty and noise present in pseudo labels and surpasses previous alternatives in terms of efficacy. Ultimately, we formulate a holistic optimization objective to learn from the hybrid supervision of scribbles and pseudo labels. Extensive experiments and evaluation on four public datasets demonstrate the superiority of our method regarding in-distribution accuracy, out-of-distribution generalization, and robustness, highlighting its promising clinical significance. Our code is available at https://***/lofrienger/S2ME.

关键词： Polyp image Segmentation Weakly-supervised Learning Spatial-Spectral Dual Branches Mutual Teaching Ensemble Learning

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Head and Neck Tumor Segmentation of MRI from Pre-and Mid-Radiotherapy with Pre-Training, Data Augmentation and Dual Flow UNet 1st

Head and Neck Tumor Segmentation of MRI from Pre-and Mid-Rad...

引用

1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Wang, Litingyu Liao, Wenjun Zhang, Shichuan Wang, Guotai University of Electronic Science and Technology of China Chengdu China Shang AI Laboratory Shanghai China Department of Radiation Oncology Sichuan Cancer Hospital & Institute Sichuan Cancer Center Chengdu China

ISBN: (纸本)9783031832734

Head and neck tumors and metastatic lymph nodes are crucial for treatment planning and prognostic analysis. Accurate segmentation and quantitative analysis of these structures require pixel-level annotation, making automated segmentation techniques essential for the diagnosis and treatment of head and neck cancer. In this study, we investigated the effects of multiple strategies on the segmentation of preradiotherapy (pre-RT) and mid-radiotherapy (mid-RT) images. For the segmentation of pre-RT images, we utilized: 1) a fully supervised learning approach, and 2) the same approach enhanced with pre-trained weights and the MixUp data augmentation technique. For mid-RT images, we introduced a novel computational-friendly network architecture that features separate encoders for mid-RT images and registered pre-RT images with their labels. the mid-RT encoder branch integrates information from pre-RT images and labels progressively during the forward propagation. We selected the highest-performing model from each fold and used their predictions to create an ensemble average for inference. In the final test, our models achieved a segmentation performance of 82.38% for pre-RT and 72.53% for mid-RT on aggregated Dice Similarity Coefficient (DSC) as HiLab. Our code is available at https://***/WltyBY/HNTS-MRG2024 train code. © the Author(s) 2025.

关键词： image segmentation

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Generative Adversarial Networks for Self-Supervised Transfer Learning in medical image Classification 5th

Generative Adversarial Networks for Self-Supervised Transfer...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Kumar, Rohit Yogeetha, B.R. Savita Mehta, Deepak School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Self-supervised transfer mastering for clinical picture analysis is a method that uses deep getting-to-know procedures to research large units of medical imaging facts without using guide labels. By way of using switch getting to know, the version can come across diffused patterns from the facts that are not easily located with the aid of the medical doctors or researchers. this method can diffuse clinical imaging packages consisting of type, segmentation, and item detection. the self-supervised transfer studying technique involves educating an artificial intelligence (AI) model on a set of scientific picas with available labels. Further, the model can detect small capabilities and patterns that may be ignored through manual labeling, leading to more excellent correct outcomes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Learning-Based US-MR Liver image Registration with Spatial Priors 25th

Learning-Based US-MR Liver Image Registration with Spatial P...

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

作者： Zeng, Qi Mohammed, Shahed Pang, Emily H. T. Schneider, Caitlin Honarvar, Mohammad Lobo, Julio Hu, Changhong Jago, James Ng, Gary Rohling, Robert Salcudean, Septimiu E. Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Vancouver Gen Hosp Vancouver BC Canada Philips Healthcare Bothell WA USA Univ British Columbia Dept Mech Engn Vancouver BC Canada

ISBN: (纸本)9783031164460;9783031164453

Registration of multi-modality images is necessary for the assessment of liver disease. In this work, we present an image registration workflow which is designed to achieve reliable alignment for subject-specific magnetic resonance (MR) and intercostal 3D ultrasound (US) images of the liver. Spatial priors modeled from the right rib segmentation are utilized to generate the initial alignment between the MR and US scans without the need of any additional tracking information. For rigid alignment, tissue segmentation models are extracted from the MR and US data with a learning-based approach to apply surface point cloud registration. Local alignment accuracy is further improved via the LC2 image similarity metric-based non-rigid registration technique. this workflow was validated with in vivo liver image data for 18 subjects. the best average thE of rigid and non-rigid registration obtained with our dataset was at 6.27 +/- 2.82 mm and 3.63 +/- 1.87 mm, respectively.

关键词： US-MR image registration Deep learning

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Momentum Contrastive Voxel-Wise Representation Learning for Semi-supervised Volumetric medical image Segmentation 1

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

作者： You, Chenyu Zhao, Ruihan Staib, Lawrence H. Duncan, James S. Yale Univ Elect Engn New Haven CT 06520 USA Univ Texas Austin Elect & Comp Engn Austin TX USA Yale Sch Med Radiol & Biomed Imaging New Haven CT USA Yale Univ Biomed Engn New Haven CT USA

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Contrastive learning (CL) aims to learn useful representation without relying on expert annotations in the context of medical image segmentation. Existing approaches mainly contrast a single positive vector (i.e., an augmentation of the same image) against a set of negatives within the entire remainder of the batch by simply mapping all input features into the same constant vector. Despite the impressive empirical performance, those methods have the following shortcomings: (1) it remains a formidable challenge to prevent the collapsing problems to trivial solutions;and (2) we argue that not all voxels within the same image are equally positive since there exist the dissimilar anatomical structures with the same image. In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions. Specifically, we first introduce a novel CL strategy to ensure feature diversity promotion among the 3D representation dimensions. We train the framework through bi-level contrastive optimization (i.e., low-level and high-level) on 3D images. Experiments on two benchmark datasets and different labeled settings demonstrate the superiority of our proposed framework. More importantly, we also prove that our method inherits the benefit of hardness-aware property from the standard CL approaches.

关键词： Contrastive learning Semi-supervised learning medical image segmentation

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Delving into Local Features for Open-Set Domain Adaptation in Fundus image Analysis 25th

Delving into Local Features for Open-Set Domain Adaptation i...

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

作者： Zhou, Yi Bai, Shaochen Zhou, Tao Zhang, Yu Fu, Huazhu Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China ASTAR Singapore Singapore

ISBN: (纸本)9783031164491;9783031164484

Unsupervised domain adaptation (UDA) has received significant attention in medical image analysis when labels are only available for the source domain data but not for the target domain. Previous UDA methods mainly focused on the closed-set scenario, assuming that only the domain distribution shifts across domains while the label space is the same. However, in practice of medical imaging, the disease categories of training data in source domain are usually limited, and the open-world target domain data may have many 'unknown classes private to the source domain. thus, open-set domain adaptation (OSDA) has great potential in this area. In this paper, we explore the OSDA problem by delving into local features for fundus disease recognition. We propose a collaborative regional clustering and alignment method to identify the common local feature patterns which are category-agnostic. then, a cluster-aware contrastive adaptation loss is introduced to adapt the distributions based on the common local features. We also construct the first fundus image benchmark for OSDA to evaluate our methods and carry out extensive experiments for comparison. It shows that our model achieves consistent improvements over the state-of-the-art methods.

关键词： image analysis

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