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检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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Kernel Attention Transformer (KAT) for Histopathology Whole Slide image Classification 25th

Kernel Attention Transformer (KAT) for Histopathology Whole ...

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

作者： Zheng, Yushan Li, Jun Shi, Jun Xie, Fengying Jiang, Zhiguo Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Engn Med Beijing 100191 Peoples R China Beihang Univ Sch Astronaut Image Proc Ctr Beijing 102206 Peoples R China Hefei Univ Technol Sch Software Hefei 230601 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Transformer has been widely used in histopathology whole slide image (WSI) classification for the purpose of tumor grading, prognosis analysis, etc. However, the design of token-wise self-attention and positional embedding strategy in the common Transformer limits the effectiveness and efficiency in the application to gigapixel histopathology images. In this paper, we propose a kernel attention Transformer (KAT) for histopathology WSI classification. the information transmission of the tokens is achieved by cross-attention between the tokens and a set of kernels related to a set of positional anchors on the WSI. Compared to the common Transformer structure, the proposed KAT can better describe the hierarchical context information of the local regions of the WSI and meanwhile maintains a lower computational complexity. the proposed method was evaluated on a gastric dataset with 2040 WSIs and an endometrial dataset with 2560 WSIs, and was compared with 6 state-of-the-art methods. the experimental results have demonstrated the proposed KAT is effective and efficient in the task of histopathology WSI classification and is superior to the state-of-the-art methods. the code is available at https://***/zhengyushan/kat.

关键词： WSI classification Transformer Cross-attention

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S³R: Self-supervised Spectral Regression for Hyperspectral Histopathology image Classification 25th

S<SUP>3</SUP>R: Self-supervised Spectral Regression for Hype...

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

作者： Xie, Xingran Wang, Yan Li, Qingli East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Benefited from the rich and detailed spectral information in hyperspectral images (HSI), HSI offers great potential for a wide variety of medical applications such as computational pathology. But, the lack of adequate annotated data and the high spatiospectral dimensions of HSIs usually make classification networks prone to overfit. thus, learning a general representation which can be transferred to the downstream tasks is imperative. To our knowledge, no appropriate self-supervised pre-training method has been designed for histopathology HSIs. In this paper, we introduce an efficient and effective Self-supervised Spectral Regression ((SR)-R-3) method, which exploits the low rank characteristic in the spectral domain of HSI. More concretely, we propose to learn a set of linear coefficients that can be used to represent one band by the remaining bands via masking out these bands. then, the band is restored by using the learned coefficients to reweight the remaining bands. Two pretext tasks are designed: (1) (SR)-R-3-CR, which regresses the linear coefficients, so that the pre-trained model understands the inherent structures of HSIs and the pathological characteristics of different morphologies;(2) (SR)-R-3-BR, which regresses the missing band, making the model to learn the holistic semantics of HSIs. Compared to prior arts i.e., contrastive learning methods, which focuses on natural images, (SR)-R-3 converges at least 3 times faster, and achieves significant improvements up to 14% in accuracy when transferring to HSI classification tasks.

关键词： Self-supervised learning Hyperspectral histopathology image classification Low-rank

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Fast FF-to-FFPE Whole Slide image Translation via Laplacian Pyramid and Contrastive Learning 25th

Fast FF-to-FFPE Whole Slide Image Translation via Laplacian ...

引用

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

作者： Fan, Lei Sowmya, Arcot Meijering, Erik Song, Yang Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

ISBN: (纸本)9783031164347;9783031164330

Formalin-Fixed Paraffin-Embedded (FFPE) and Fresh Frozen (FF) are two major types of histopathological Whole Slide images (WSIs). FFPE provides high-quality images, however the acquisition process usually takes 12 to 48 h, while FF with relatively low-quality images takes less than 15 min to acquire. In this work, we focus on the task of translating FF to FFPE style (FF2FFPE), to synthesize FFPE-style images from FF images. However, WSIs with giga-pixels impose heavy constraints on computation and time resources. To address these issues, we propose the fastFF2FFPE for translating FF into FFPE-style efficiently. Specifically, we decompose FF images into low- and high-frequency components based on the Laplacian Pyramid, wherein the low-frequency component at low resolution is transformed into FFPEstyle with low computational cost, and the high-frequency component is used for providing details. We further employ contrastive learning to encourage similarities between original and output patches. We conduct FF2FFPE translation experiments on the Cancer Genome Atlas (TCGA) Glioblastoma Multiforme (GBM) and Lung Squamous Cell Carcinoma (LUSC) datasets, and verify the efficacy of our model on Microsatellite Instability prediction in gastrointestinal cancer. the code and models are released at https://***/hellodfan/fastFF2FFPE.

关键词： image translation Histopathology Laplacian pyramid Whole Slide images Contrastive learning

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation 15th

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th international Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. these methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. this results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. the test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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A Comprehensive Analysis of Ultrasound image Processing Methods 5

A Comprehensive Analysis of Ultrasound Image Processing Meth...

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5th international conference on Contemporary computing and Informatics, IC3I 2022

作者： Pandilwar, Vaibhav Rakhra, Manik Deshmukh, Sanket Singh, Arun Singh, Dalwinder Lovely Professional University School of Computer Science and Engineering Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350398267

the medical pictures that are utilized for medical purposes include things like ultrasounds, which provide such images. the ultrasound scans displayed a variety of approaches that may be utilized for the calcification of breast cancer. the calcification of breast cancer is analyzed with the help of the ultrasound pictures. A number of processes, such as pre-processing, segmentation, feature extraction, and masking, are included in the breast cancer calcification method. the cancerous part of the input picture will be hidden when the calcification procedure is applied. the findings will be examined using criteria such as recall, accuracy, and precision. © 2022 IEEE.

关键词： Ultrasonics

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MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble 1st

MRI-Based Head and Neck Tumor Segmentation Using nnU-Net wit...

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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： Mol, Frank N. Hoek, Luuk van der Ma, Baoqiang Nagam, Bharath Chowdhary Sijtsema, Nanna M. van Dijk, Lisanne V. Bunte, Kerstin Vlijm, Rifka Van Ooijen, Peter M. A. Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands University of Groningen University Medical Center Groningen Groningen9700 RB Netherlands

ISBN: (纸本)9783031832734

the superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment planning. the Head and Neck Tumor Segmentation for MR-Guided Applications chal- lenge (HNTSMRG-24) comprises two tasks: segmentation of primary gross tumor volume (GTVp) and metastatic lymph nodes (GTVn) on T2- weighted MRI volumes obtained at (1) pre-radiotherapy (pre-RT) and (2) mid-radiotherapy (mid-RT). the training dataset consists of data from 150 patients, including MRI volumes of pre-RT, mid-RT, and pre-RT registered to the corresponding mid-RT volumes. Each MRI volume is accompanied by a label mask, generated by merging independent annotations from a minimum of three experts. For both tasks, we propose adopt- ing the nnU-Net V2 framework by the use of a 15-fold cross-validation ensemble instead of the standard number of 5 folds for increased robust- ness and variability. For pre-RT segmentation, we augmented the initial training data (150 pre-RT volumes and masks) with the corresponding mid-RT data. For mid-RT segmentation, we opted for a three-channel input, which, in addition to the mid-RT MRI volume, comprises the registered pre-RT MRI volume and the corresponding mask. the mean of the aggregated Dice Similarity Coefficient for GTVp and GTVn is computed on a blind test set and determines the quality of the proposed methods. these metrics determine the final ranking of methods for both tasks separately. the final blind testing (50 patients) of the methods proposed by our team, RUG_UMCG, resulted in an aggregated Dice Similarity Coefficient of 0.81 (0.77 for GTVp and 0.85 for GTVn) for Task 1 and 0.70 (0.54 for GTVp and 0.86 for GTVn) for Task 2. © the Author(s) 2025.

关键词： image segmentation

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On the Dataset Quality Control for image Registration Evaluation 25th

On the Dataset Quality Control for Image Registration Evalua...

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

作者： Luo, Jie Ma, Guangshen Haouchine, Nazim Xu, Zhe Wang, Yixin Kapur, Tina Ning, Lipeng Wells, William M., III Frisken, Sarah Harvard Med Sch Brigham & Womens Hosp Boston MA USA Duke Univ Dept Mech Engn & Mat Sci Durham NC USA Chinese Univ Hong Kong Dept Biomed Engn Hong Kong Peoples R China Stanford Univ Dept Bioengn Stanford CA USA

ISBN: (纸本)9783031164460;9783031164453

Current registration evaluations typically compute target registration error using manually annotated datasets. As a result, the quality of landmark annotations is crucial for unbiased comparisons because registration algorithms are trained and tested using these landmarks. Even though some data providers claim to have mitigated the inter-observer variability by having multiple raters, quality control such as a third-party screening can still be reassuring for intended users. Examining the landmark quality for neurosurgical datasets (RESECT and BITE) poses specific challenges. In this study, we applied the variogram, which is a tool extensively used in geostatistics, to convert 3D landmark distributions into an intuitive 2D representation. this allowed us to identify potential problematic cases efficiently so that they could be examined by experienced radiologists. In both the RESECT and BITE datasets, we identified and confirmed a small number of landmarks with potential localization errors and found that, in some cases, the landmark distribution was not ideal for an unbiased assessment of non-rigid registration errors. Under discussion, we provide some constructive suggestions for improving the utility of publicly available annotated data.

关键词： image registration Public datasets Quality control

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End-to-End Learning for image-Based Detection of Molecular Alterations in Digital Pathology 25th

End-to-End Learning for Image-Based Detection of Molecular A...

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

作者： Teichmann, Marvin Aichert, Andre Bohnenberger, Hanibal Stroebel, Philipp Heimann, Tobias Siemens Healthineers Digital Technol & Innovat Erlangen Germany Univ Med Ctr Goettingen Inst Pathol Gottingen Germany

ISBN: (纸本)9783031164347;9783031164330

Current approaches for classification of whole slide images (WSI) in digital pathology predominantly utilize a two-stage learning pipeline. the first stage identifies areas of interest (e.g. tumor tissue), while the second stage processes cropped tiles from these areas in a supervised fashion. During inference, a large number of tiles are combined into a unified prediction for the entire slide. A major drawback of such approaches is the requirement for task-specific auxiliary labels which are not acquired in clinical routine. We propose a novel learning pipeline for WSI classification that is trainable end-to-end and does not require any auxiliary annotations. We apply our approach to predict molecular alterations for a number of different use-cases, including detection of microsatellite instability in colorectal tumors and prediction of specific mutations for colon, lung, and breast cancer cases from the Cancer Genome Atlas. Results reach AUC scores of up to 94% and are shown to be competitive with state of the art two-stage pipelines. We believe our approach can facilitate future research in digital pathology and contribute to solve a large range of problems around the prediction of cancer phenotypes, hopefully enabling personalized therapies for more patients in future.

关键词： Digital pathology Whole slide image (wsi) classification Pan-cancer genetic alterations Histopathology End-to-end learning

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Identifying and Combating Bias in Segmentation Networks by Leveraging Multiple Resolutions 25th

Identifying and Combating Bias in Segmentation Networks by L...

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

作者： Henschel, Leonie Kuegler, David Andrews, Derek S. Nordahl, Christine W. Reuter, Martin German Ctr Neurodegenerat Dis DZNE Bonn Germany Univ Calif Davis MIND Inst Davis CA USA Univ Calif Davis Dept Psychiat & Behav Sci Davis CA USA Harvard Med Sch Dept Radiol Boston MA 02115 USA Mass Gen Hosp AA Martinos Ctr Biomed Imag Boston MA 02114 USA

ISBN: (纸本)9783031164439;9783031164422

Exploration of bias has significant impact on the transparency and applicability of deep learning pipelines in medical settings, yet is so far woefully understudied. In this paper, we consider two separate groups for which training data is only available at differing image resolutions. For group H, available images and labels are at the preferred high resolution while for group L only deprecated lower resolution data exist. We analyse how this resolution-bias in the data distribution propagates to systematically biased predictions for group L at higher resolutions. Our results demonstrate that single-resolution training settings result in significant loss of volumetric group differences that translate to erroneous segmentations as measured by DSC and subsequent classification failures on the low resolution group. We further explore how training data across resolutions can be used to combat this systematic bias. Specifically, we investigate the effect of image resampling, scale augmentation and resolution independence and demonstrate that biases can effectively be reduced with multi-resolution approaches.

关键词： image resolution

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LifeLonger: A Benchmark for Continual Disease Classification 25th

LifeLonger: A Benchmark for Continual Disease Classification

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

作者： Derakhshani, Mohammad Mahdi Najdenkoska, Ivona Van Sonsbeek, Tom Zhen, Xiantong Mahapatra, Dwarikanath Worring, Marcel Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands Inception Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031164347;9783031164330

Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing newly annotated medical data from different sources. To exploit the incoming streams of data, these models would benefit largely from sequentially learning from new samples, without forgetting the previously obtained knowledge. In this paper we introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection, by applying existing state-of-the-art continual learning methods. In particular, we consider three continual learning scenarios, namely, task and class incremental learning and the newly defined cross-domain incremental learning. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge. We perform a thorough analysis of the performance and examine how the well-known challenges of continual learning, such as the catastrophic forgetting exhibit themselves in this setting. the encouraging results demonstrate that continual learning has a major potential to advance disease classification and to produce a more robust and efficient learning framework for clinical settings. the code repository, data partitions and baseline results for the complete benchmark are publicly available(1) (https://***/mmderalchshani/LifeLonger).

关键词： medical continual learning Disease classification medical image analysis

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