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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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Pathology-and-Genomics Multimodal Transformer for Survival Outcome Prediction 26th

Pathology-and-Genomics Multimodal Transformer for Survival O...

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

作者： Ding, Kexin Zhou, Mu Metaxas, Dimitris N. Zhang, Shaoting UNC Charlotte Dept Comp Sci Charlotte NC USA Rutgers State Univ Dept Comp Sci Piscataway NJ USA Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Survival outcome assessment is challenging and inherently associated with multiple clinical factors (e.g., imaging and genomics biomarkers) in cancer. Enabling multimodal analytics promises to reveal novel predictive patterns of patient outcomes. In this study, we propose a multimodal transformer (PathOmics) integrating pathology and genomics insights into colon-related cancer survival prediction. We emphasize the unsupervised pretraining to capture the intrinsic interaction between tissue microenvironments in gigapixel whole slide images (WSIs) and a wide range of genomics data (e.g., mRNA-sequence, copy number variant, and methylation). After the multimodal knowledge aggregation in pretraining, our task-specific model finetuning could expand the scope of data utility applicable to both multi- and single-modal data (e.g., image- or genomics-only). We evaluate our approach on both TCGA colon and rectum cancer cohorts, showing that the proposed approach is competitive and outperforms state-of-the-art studies. Finally, our approach is desirable to utilize the limited number of finetuned samples towards data-efficient analytics for survival outcome prediction. the code is available at https://***/Cassie07/PathOmics.

关键词： Histopathological image analysis Multimodal learning Cancer diagnosis Survival prediction

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Unified Brain MR-Ultrasound Synthesis Using Multi-modal Hierarchical Representations 26th

Unified Brain MR-Ultrasound Synthesis Using Multi-modal Hier...

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

作者： Dorent, Reuben Haouchine, Nazim Kogl, Fryderyk Joutard, Samuel Juvekar, Parikshit Torio, Erickson Golby, Alexandra J. Ourselin, Sebastien Frisken, Sarah Vercauteren, Tom Kapur, Tina Wells, William M., III Harvard Med Sch Brigham & Womens Hosp Boston MA 02115 USA Kings Coll London London England MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9783031439988;9783031439995

We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical latent structure, we introduce a probabilistic formulation for fusing multi-modal images in a common latent representation while having the flexibility to handle incomplete image sets as input. Moreover, adversarial learning is employed to generate sharper images. Extensive experiments are performed on the challenging problem of joint intra-operative ultrasound (iUS) and Magnetic Resonance (MR) synthesis. Our model outperformed multi-modal VAEs, conditional GANs, and the current state-of-the-art unified method (ResViT) for synthesizing missing images, demonstrating the advantage of using a hierarchical latent representation and a principled probabilistic fusion operation. Our code is publicly available (https://***/ReubenDo/MHVAE).

关键词： Variational Auto-Encoder Ultrasound Brain Resection image Synthesis

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Improved Flexibility and Interpretability of Large Vessel Stroke Prognostication Using image Synthesis and Multi-task Learning 26th

Improved Flexibility and Interpretability of Large Vessel St...

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

作者： Zeng, Minyan Xie, Yutong To, Minh-Son Oakden-Rayner, Lauren Whitbread, Luke Bacchi, Stephen Bird, Alix Smith, Luke Scroop, Rebecca Kleinig, Timothy Jannes, Jim Palmer, Lyle J. Jenkinson, Mark Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Univ Adelaide Sch Publ Hlth Adelaide SA Australia Flinders Univ S Australia Flinders Hlth & Med Res Inst Bedford Pk SA Australia Royal Adelaide Hosp Adelaide SA Australia Univ Adelaide Sch Comp & Math Sci Adelaide SA Australia South Australian Hlth & Med Res Inst Adelaide SA Australia

ISBN: (纸本)9783031439032;9783031439049

While acute ischemic stroke due to large vessel occlusion (LVO) may be life-threatening or permanently disabling, timely intervention with endovascular thrombectomy (EVT) can prove life-saving for affected patients. Appropriate patient selection based on prognostic prediction is vital for this costly and invasive procedure, as not all patients will benefit from EVT. Accurate prognostic prediction for LVO presents a significant challenge. Computed Tomography Perfusion (CTP) maps can provide additional information for clinicians to make decisions. However, CTP maps are not always available due to variations in available equipment, funding, expertise and image quality. To address these gaps, we test (i) the utility of acquired CTP maps in a deep learning prediction model, (ii) the ability to improve flexibility of this model through image synthesis, and (iii) the added benefits of including multi-task learning with a simple clinical task to focus the synthesis on key clinical features. Our results demonstrate that network architectures utilising a full set of images can still be flexibly deployed if CTP maps are unavailable as their benefits can be effectively synthesized from more widely available images (NCCT and CTA). Additionally, such synthesized images may help with interpretability and building a clinically trusted model.

关键词： Forecasting

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CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological image Classification 26th

CellGAN: Conditional Cervical Cell Synthesis for Augmenting ...

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

作者： Shen, Zhenrong Cao, Maosong Wang, Sheng Zhang, Lichi Wang, Qian Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Automatic examination of thin-prep cytologic test (TCT) slides can assist pathologists in finding cervical abnormality for accurate and efficient cancer screening. Current solutions mostly need to localize suspicious cells and classify abnormality based on local patches, concerning the fact that whole slide images of TCT are extremely large. It thus requires many annotations of normal and abnormal cervical cells, to supervise the training of the patch-level classifier for promising performance. In this paper, we propose CellGAN to synthesize cytopathological images of various cervical cell types for augmenting patch-level cell classification. Built upon a lightweight backbone, CellGAN is equipped with a non-linear class mapping network to effectively incorporate cell type information into image generation. We also propose the Skip-layer Global Context module to model the complex spatial relationship of the cells, and attain high fidelity of the synthesized images through adversarial learning. Our experiments demonstrate that CellGAN can produce visually plausible TCT cytopathological images for different cell types. We also validate the effectiveness of using CellGAN to greatly augment patch-level cell classification performance. Our code and model checkpoint are available at https://***/ZhenrongShen/CellGAN.

关键词： Conditional image Synthesis Generative Adversarial Network Cytopathological image Classification Data Augmentation

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Quantifying Knee Cartilage Shape and Lesion: From image to Metrics 3rd

Quantifying Knee Cartilage Shape and Lesion: From Image to...

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3rd international Workshop on Applications of medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Yao, Yongcheng Chen, Weitian UKRI CDT in Biomedical AI School of Informatics The University of Edinburgh Edinburgh United Kingdom Department of Imaging and Interventional Radiology The Chinese University of Hong Kong China

ISBN: (纸本)9783031820069

Imaging features of knee articular cartilage have been shown to be potential imaging biomarkers for knee osteoarthritis. Despite recent methodological advancements in image analysis techniques like image segmentation, registration, and domain-specific image computing algorithms, only a few works focus on building fully automated pipelines for imaging feature extraction. In this study, we developed a deep-learning-based medical image analysis application for knee cartilage morphometrics, CartiMorph Toolbox (CMT). We proposed a 2-stage joint template learning and registration network, CMT-reg. We trained the model using the OAI-ZIB dataset and assessed its performance in template-to-image registration. the CMT-reg demonstrated competitive results compared to other state-of-the-art models. We integrated the proposed model into an automated pipeline for the quantification of cartilage shape and lesion (full-thickness cartilage loss, specifically). the toolbox provides a comprehensive, user-friendly solution for medical image analysis and data visualization. the software and models are available at https://***/YongchengYAO/CMT-AMAI24paper. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cartilage

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Learning Large Margin Sparse Embeddings for Open Set medical Diagnosis 26th

Learning Large Margin Sparse Embeddings for Open Set Medical...

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

作者： Liu, Mingyuan Xu, Lu Zhang, Jicong Beihang Univ Sch Biol Sci & Med Engn Beijing Peoples R China Beihang Univ Hefei Innovat Res Inst Hefei Anhui Peoples R China

ISBN: (纸本)9783031439926;9783031439933

Fueled by deep learning, computer-aided diagnosis achieves huge advances. However, out of controlled lab environments, algorithms could face multiple challenges. Open set recognition (OSR), as an important one, states that categories unseen in training could appear in testing. In medical fields, it could derive from incompletely collected training datasets and the constantly emerging new or rare diseases. OSR requires an algorithm to not only correctly classify known classes, but also recognize unknown classes and forward them to experts for further diagnosis. To tackle OSR, we assume that known classes could densely occupy small parts of theembedding space and the remaining sparse regions could be recognized as unknowns. Following it, we propose Open Margin Cosine Loss (OMCL) unifying two mechanisms. the former, called Margin Loss with Adaptive Scale (MLAS), introduces angular margin for reinforcing intraclass compactness and inter-class separability, together with an adaptive scaling factor to strengthen the generalization capacity. the latter, called Open-Space Suppression (OSS), opens the classifier by recognizing sparse embedding space as unknowns using proposed feature space descriptors. Besides, since medical OSR is still a nascent field, two publicly available benchmark datasets are proposed for comparison. Extensive ablation studies and feature visualization demonstrate the effectiveness of each design. Compared with state-of-the-art methods, MLAS achieves superior performances, measured by ACC, AUROC, and OSCR.

关键词： Open set recognition computer aided diagnosis image classification

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Detection-Free Pipeline for Cervical Cancer Screening of Whole Slide images 26th

Detection-Free Pipeline for Cervical Cancer Screening of Who...

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

作者： Cao, Maosong Fei, Manman Cai, Jiangdong Liu, Luyan Zhang, Lichi Wang, Qian ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Cervical cancer is a significant health burden worldwide, and computer-aided diagnosis (CAD) pipelines have the potential to improve diagnosis efficiency and treatment outcomes. However, traditional CAD pipelines have limitations due to the requirement of a detection model trained on a large annotated dataset, which can be expensive and time-consuming. they also have a clear performance limit and low data utilization efficiency. To address these issues, we introduce a two-stage detection-free pipeline, incorporating pooling transformer andMoCo pre-training strategies, that optimizes data utilization for whole slide images (WSIs) while relying solely on sample-level diagnosis labels for training. the experimental results demonstrate the effectiveness of our approach, with performance scaling up as the amount of data increases. Overall, our novel pipeline has the potential to fully utilize massive data in WSI classification and can significantly improve cancer diagnosis and treatment. By reducing the reliance on expensive data labeling and detection models, our approach could enable more widespread and cost-effective implementation of CAD pipelines in clinical settings. Our code and model is available at https://***/thebestannie/Detection-free-MICCAI2023.

关键词： Detection-free Contrastive Learning Pathology image Classification Cervical Cancer

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DWU-Net: A Dual-Branch U-Shaped Multi-Modal medical image Segmentation Network Based on Wavelet Transform 1st

DWU-Net: A Dual-Branch U-Shaped Multi-Modal Medical Image Se...

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1st MICCAI Challenge Comprehensive Analysis and computing of Real-World medical images, CARE 2024 Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Li, Jihao Han, Xian-Hua Qiao, Xu Sun, Jiande Wang, Jian School of Information Science and Engineering Shandong Normal University Jinan China Graduate School of Artificial Intelligence and Science Rikkyo University Tokyo Japan School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9783031870088

Cardiovascular disease is one of the most difficult diseases to treat. Segmentation of cardiac images is an essential approach to improve the effectiveness and accuracy of cardiovascular disease treatment and intervention. Cardiac image segmentation, however, remains a challenging task, especially for effectively and accurately segmenting the multimodal images and simultaneously segmenting multiple heart substructures. In this paper, we propose a dual-branch U-shaped network based on wavelet transform, called DWU-Net. First, we convert the multimodal medical images into low-frequency and high-frequency images using wavelet transform to narrow the multimodal feature differences. the network consists of two branches of U-shaped sub-network, which accepting low-frequency and high-frequency images, respectively. Low-frequency and high-frequency features extracted by the corresponding branch are fused in deep levels and the final segmentation is achieved by both supervising on segmentation results from fused features and self-supervising among segmentation results from the two branches. the proposed model was verified on the Whole Heart Segmentation ++ (WHS++) dataset, showing greatly improved segmentation results compared with state-of-the-art methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Explainable image Classification with Improved Trustworthiness for Tissue Characterisation 26th

Explainable Image Classification with Improved Trustworthine...

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

作者： Roddan, Alfie Xu, Chi Ajlouni, Serine Kakaletri, Irini Charalampaki, Patra Giannarou, Stamatia Imperial Coll London Dept Surg & Canc Hamlyn Ctr Robot Surg London England Univ Witten Herdecke Fac Med Witten Germany Rhein Friedrich Wilhelms Univ Bonn Fac Med Bonn Germany Cologne Med Ctr Dept Neurosurg Cologne Germany

ISBN: (纸本)9783031438943;9783031438950

the deployment of Machine Learning models intraoperatively for tissue characterisation can assist decision making and guide safe tumour resections. For the surgeon to trust the model, explainability of the generated predictions needs to be provided. For image classification models, pixel attribution (PA) and risk estimation are popular methods to infer explainability. However, the former method lacks trustworthiness while the latter can not provide visual explanation of the model's attention. In this paper, we propose the first approach which incorporates risk estimation into a PA method for improved and more trustworthy image classification explainability. the proposed method iteratively applies a classification model with a PA method to create a volume of PA maps. We introduce a method to generate an enhanced PA map by estimating the expectation values of the pixel-wise distributions. In addition, the coefficient of variation (CV) is used to estimate pixel-wise risk of this enhanced PA map. Hence, the proposed method not only provides an improved PA map but also produces an estimation of risk on the output PA values. Performance evaluation on probe-based Confocal Laser Endomicroscopy (pCLE) data verifies that our improved explainability method outperforms the state-of-the-art.

关键词： Explainability Uncertainty MC Dropout

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DiMix: Disentangle-and-Mix Based Domain Generalizable medical image Segmentation 1

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

作者： Kim, Hyeongyu Shin, Yejee Hwang, Dosik Yonsei Univ Sch Elect & Elect Engn Seoul South Korea Probe Med Seoul South Korea Yonsei Univ Dept Radiol Seoul South Korea Yonsei Univ Ctr Clin Imaging Data Sci Seoul South Korea Yonsei Univ Dept Oral & Maxillofacial Radiol Coll Dent Seoul South Korea Korea Inst Sci & Technol Ctr Healthcare Robot Seoul South Korea

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

the rapid advancements in deep learning have revolutionized multiple domains, yet the significant challenge lies in effectively applying this technology to novel and unfamiliar environments, particularly in specialized and costly fields like medicine. Recent deep learning research has therefore focused on domain generalization, aiming to train models that can perform well on datasets from unseen environments. this paper introduces a novel framework that enhances generalizability by leveraging transformer-based disentanglement learning and style mixing. Our framework identifies features that are invariant across different domains. through a combination of content-style disentanglement and image synthesis, the proposed method effectively learns to distinguish domain-agnostic features, resulting in improved performance when applied to unseen target domains. To validate the effectiveness of the framework, experiments were conducted on a publicly available Fundus dataset, and comparative analyses were performed against other existing approaches. the results demonstrated the power and efficacy of the proposed framework, showcasing its ability to enhance domain generalization performance.

关键词： Domain generalization medical image segmentation Disentanglement Transformers

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