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

共 2874 条记录，以下是211-220 订阅

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Edge Segmentation based on Illumination Invariant Feature Detector Phase Congruency 5

Edge Segmentation based on Illumination Invariant Feature De...

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5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Patil, Rajendra V. Aggarwal, Renu Shivaji Deore, Shailesh Sunrise University Department of Computer Engineering Rajasthan Alwar India SunRise University Department of Computer Science and Engineering Rajasthan Alwar India SSVPS BSD College of Engineering Department of Computer Engineering Dhule India

ISBN: (纸本)9798350395235

image Segmentation is necessary stage in image investigation. In computer vision, image segmentation is the process of dividing digital image into multiple segments. Dividing the image into various parts simplifies the analysis of image. In numerous image analysis and computer visualization uses, including remote sensing, robot vision, medical image analysis, and pattern recognition, the subdivision of image into segments is an important preprocessing stage. Finding the outlines of objects in the images is the fundamental step in the segmentation of pictures. Zero crossover, Canny, Gradient dependent, zero cross, Laplacian are examples of traditional edge detection techniques. this study presents a novel method based on phase congruency for edge segmentation. As compared to traditional edge detection methods, advantage of phase congruency is its invariance to uneven illumination. To obtain edge segmentation outcomes, edge lines were obtained using phase congruency. Long edges give an approximate sense of the object's arrangement in the picture. Color likeness between edges is determined while short edges are removed. Color similar edges assigned same label. Performance analysis shows that proposed method produces good results for synthetic images and natural real world images in which objects are well separated by boundaries whereas it results in broken edges for images having complex structure. © 2024 IEEE.

关键词： image segmentation

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I2M2Net: Inter/Intra-modal Feature Masking Self-distillation for Incomplete Multimodal Skin Lesion Diagnosis 9th

I2M2Net: Inter/Intra-modal Feature Masking Self-distillation...

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

作者： Wang, Ke Qiu, Linwei Zhang, Yilan Xie, Fengying Beihang Univ Sch Astronaut Image Proc Ctr Beijing 100191 Peoples R China King Abdullah Univ Sci & Technol KAUST Thuwal 239556900 Saudi Arabia

ISBN: (纸本)9783031776090;9783031776106

Multimodal learning has demonstrated promising advantages over single-modal approaches in the diagnosis of skin lesions. However, these methods often suffer from significant accuracy degradation when encountering missing modalities, hindering their clinical deployment. In this paper, we introduce a novel and effective framework, I2M2Net, for incomplete multimodal learning, focusing on adaptively and progressively mining knowledge about modal feature-aware combinations. Specifically, one branch conducts normal classification using the original complete multimodal features extracted by heterogeneous modal encoders, while another branch shares the same structures and weights, designed to perform self-distillation with masked modality combinations. these combinations are imposed on the complete features using two masking strategies simultaneously: 1) random dropout of modality (i.e. inter-modal feature masking) to simulate different missing modality combinations and foster combination-invariant dependencies, and 2) randomly mask patches of the remaining modal features (i.e. intra-modal feature masking) to promote combination-specific representations. Additionally, we design a combination-based curriculum learning (CCL) algorithm to identify weak combinations and progressively guide our network to facilitate incomplete modality learning on challenging combinations. this is achieved by adaptively adjusting the probabilities of masking based on the consistency between the complete combination and other combinations. Experimental results on the multimodal skin disease dataset Derm7pt demonstrate that our method outperforms other state-of-the-art approaches.

关键词： Missing modality Multi-modal learning Skin lesion diagnosis

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Few-Shot medical image Segmentation via a Region-Enhanced Prototypical Transformer 26th

Few-Shot Medical Image Segmentation via a Region-Enhanced Pr...

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

作者： Zhu, Yazhou Wang, Shidong Xin, Tong Zhang, Haofeng Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Newcastle Univ Sch Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England Newcastle Univ Sch Comp Newcastle Upon Tyne NE1 7RU Tyne & Wear England

ISBN: (纸本)9783031439001;9783031439018

Automated segmentation of large volumes of medical images is often plagued by the limited availability of fully annotated data and the diversity of organ surface properties resulting from the use of different acquisition protocols for different patients. In this paper, we introduce a more promising few-shot learning-based method named Region-enhanced Prototypical Transformer (RPT) to mitigate the effects of large intraclass diversity/bias. First, a subdivision strategy is introduced to produce a collection of regional prototypes from the foreground of the support prototype. Second, a self-selection mechanism is proposed to incorporate into the Bias-alleviated Transformer (BaT) block to suppress or remove interferences present in the query prototype and regional support prototypes. By stacking BaT blocks, the proposed RPT can iteratively optimize the generated regional prototypes and finally produce rectified and more accurate global prototypes for Few-Shot medical image Segmentation (FSMS). Extensive experiments are conducted on three publicly available medical image datasets, and the obtained results show consistent improvements compared to state-of-the-art FSMS methods. the source code is available at: https://***/YazhouZhu19/RPT.

关键词： Few-Shot Learning medical image Segmentation Bias Alleviation Transformer

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Pick the Best Pre-trained Model: Towards Transferability Estimation for medical image Segmentation 26th

Pick the Best Pre-trained Model: Towards Transferability Est...

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

作者： Yang, Yuncheng Wei, Meng He, Junjun Yang, Jie Ye, Jin Gu, Yun Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Transfer learning is a critical technique in training deep neural networks for the challenging medical image segmentation task that requires enormous resources. With the abundance of medical image data, many research institutions release models trained on various datasets that can form a huge pool of candidate source models to choose from. Hence, it's vital to estimate the source models' transferability (i.e., the ability to generalize across different downstream tasks) for proper and efficient model reuse. To make up for its deficiency when applying transfer learning to medical image segmentation, in this paper, we therefore propose a new Transferability Estimation (TE) method. We first analyze the drawbacks of using the existing TE algorithms for medical image segmentation and then design a source-free TE framework that considers both class consistency and feature variety for better estimation. Extensive experiments show that our method surpasses all current algorithms for transferability estimation in medical image segmentation. Code is available at here.

关键词： Transferability Estimation Model Selection medical image Analysis Deep Learning

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Federated Model Aggregation via Self-supervised Priors for Highly Imbalanced medical image Classification 26th

Federated Model Aggregation via Self-supervised Priors for H...

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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： Elbatel, Marawan Wang, Hualiang Mart, Robert Fu, Huazhu Li, Xiaomeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Girona Comp Vis & Robot Inst Girona Spain ASTAR Inst High Performance Comp IHPC Singapore Singapore

ISBN: (纸本)9783031474002;9783031474019

In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners. In this paper, we study the interclient intra-class variations with publicly available self-supervised auxiliary networks. Specifically, we find that employing a shared auxiliary pre-trained model, like MoCo-V2, locally on every client yields consistent divergence measurements. Based on these findings, we derive a dynamic balanced model aggregation via self-supervised priors (MAS) to guide the global model optimization. Fed-MAS can be utilized with different local learning methods for effective model aggregation toward a highly robust and unbiased global model. Our code is available at https://***/xmed-lab/Fed-MAS.

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SwIPE: Efficient and Robust medical image Segmentation with Implicit Patch Embeddings 26th

SwIPE: Efficient and Robust Medical Image Segmentation with ...

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

作者： Zhang, Yejia Gu, Pengfei Sapkota, Nishchal Chen, Danny Z. Univ Notre Dame Notre Dame IN 46556 USA

ISBN: (纸本)9783031439032;9783031439049

Modern medical image segmentation methods primarily use discrete representations in the form of rasterized masks to learn features and generate predictions. Although effective, this paradigm is spatially inflexible, scales poorly to higher-resolution images, and lacks direct understanding of object shapes. To address these limitations, some recent works utilized implicit neural representations (INRs) to learn continuous representations for segmentation. However, these methods often directly adopted components designed for 3D shape reconstruction. More importantly, these formulations were also constrained to either point-based or global contexts, lacking contextual understanding or local fine-grained details, respectively-both critical for accurate segmentation. To remedy this, we propose a novel approach, SwIPE (Segmentation with Implicit Patch Embeddings), that leverages the advantages of INRs and predicts shapes at the patch level-rather than at the point level or image level-to enable both accurate local boundary delineation and global shape coherence. Extensive evaluations on two tasks (2D polyp segmentation and 3D abdominal organ segmentation) show that SwIPE significantly improves over recent implicit approaches and outperforms state-of-the-art discrete methods with over 10x fewer parameters. Our method also demonstrates superior data efficiency and improved robustness to data shifts across image resolutions and datasets. Code is available on Github.

关键词： medical image Segmentation Deep Implicit Shape Representations Patch Embeddings Implicit Shape Regularization

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AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation Datasets 26th

AViT: Adapting Vision Transformers for Small Skin Lesion Seg...

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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： Du, Siyi Bayasi, Nourhan Hamarneh, Ghassan Garbi, Rafeef Univ British Columbia Vancouver BC Canada Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9783031474002;9783031474019

Skin lesion segmentation (SLS) plays an important role in skin lesion analysis. Vision transformers (ViTs) are considered an auspicious solution for SLS, but they require more training data compared to convolutional neural networks (CNNs) due to their inherent parameterheavy structure and lack of some inductive biases. To alleviate this issue, current approaches fine-tune pre-trained ViT backbones on SLS datasets, aiming to leverage the knowledge learned from a larger set of natural images to lower the amount of skin training data needed. However, fully fine-tuning all parameters of large backbones is computationally expensive and memory intensive. In this paper, we propose AViT, a novel efficient strategy to mitigate ViTs' data-hunger by transferring any pretrained ViTs to the SLS task. Specifically, we integrate lightweight modules (adapters) within the transformer layers, which modulate the feature representation of a ViT without updating its pre-trained weights. In addition, we employ a shallow CNN as a prompt generator to create a prompt embedding from the input image, which grasps fine-grained information and CNN's inductive biases to guide the segmentation task on small datasets. Our quantitative experiments on 4 skin lesion datasets demonstrate that AViT achieves competitive, and at times superior, performance to SOTA but with significantly fewer trainable parameters. Our code is available at https://***/siyi-wind/AViT.

关键词： Vision Transformer Data-efficiency Efficiency medical image Segmentation Dermatology

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MedNeXt: Transformer-Driven Scaling of ConvNets for medical image Segmentation 26th

MedNeXt: Transformer-Driven Scaling of ConvNets for Medical ...

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

作者： Roy, Saikat Koehler, Gregor Ulrich, Constantin Baumgartner, Michael Petersen, Jens Isensee, Fabian Jaeger, Paul F. Maier-Hein, Klaus H. German Canc Res Ctr Div Med Image Comp MIC Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany NCT Heidelberg Natl Ctr Tumor Dis NCT Heidelberg Germany German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany

ISBN: (纸本)9783031439001;9783031439018

there has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging. Convolutional networks, in contrast, have higher inductive biases and consequently, are easily trainable to high performance. Recently, the ConvNeXt architecture attempted to modernize the standard ConvNet by mirroring Transformer blocks. In this work, we improve upon this to design a modernized and scalable convolutional architecture customized to challenges of data-scarce medical settings. We introduce MedNeXt, a Transformer-inspired large kernel segmentation network which introduces - 1) A fully ConvNeXt 3D Encoder-Decoder Network for medical image segmentation, 2) Residual ConvNeXt up and downsampling blocks to preserve semantic richness across scales, 3) A novel technique to iteratively increase kernel sizes by upsampling small kernel networks, to prevent performance saturation on limited medical data, 4) Compound scaling at multiple levels (depth, width, kernel size) of MedNeXt. this leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation. Our code is made publicly available at: https://***/MIC-DKFZ/MedNeXt.

关键词： medical image Segmentation Transformers MedNeXt Large Kernels ConvNeXt

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Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning 26th

Deep Homography Prediction for Endoscopic Camera Motion Imit...

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

作者： Huber, Martin Ourselin, Sebastien Bergeles, Christos Vercauteren, Tom Kings Coll London Sch Biomed Engn & Image Sci London England

ISBN: (纸本)9783031439957;9783031439964

In this work, we investigate laparoscopic camera motion automation through imitation learning from retrospective videos of laparoscopic interventions. A novel method is introduced that learns to augment a surgeon's behavior in image space through object motion invariant image registration via homographies. Contrary to existing approaches, no geometric assumptions are made and no depth information is necessary, enabling immediate translation to a robotic setup. Deviating from the dominant approach in the literature which consist of following a surgical tool, we do not handcraft the objective and no priors are imposed on the surgical scene, allowing the method to discover unbiased policies. In this new research field, significant improvements are demonstrated over two baselines on the Cholec80 and HeiChole datasets, showcasing an improvement of 47% over camera motion continuation. the method is further shown to indeed predict camera motion correctly on the public motion classification labels of the AutoLaparo dataset. All code is made accessible on Github (https://***/RViMLab/homography_imitation_learning).

关键词： computer vision Robotic surgery Imitation learning

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Evaluation and Improvement of Segment Anything Model for Interactive Histopathology image Segmentation 26th

Evaluation and Improvement of Segment Anything Model for Int...

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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： Kim, SeungKyu Oh, Hyun-Jic Min, Seonghui Jeong, Won-Ki Korea Univ Dept Comp Sci & Engn Coll Informat Seoul South Korea

ISBN: (纸本)9783031474002;9783031474019

With the emergence of the Segment Anything Model (SAM) as a foundational model for image segmentation, its application has been extensively studied across various domains, including the medical field. However, its potential in the context of histopathology data, specifically in region segmentation, has received relatively limited attention. In this paper, we evaluate SAM's performance in zero-shot and fine-tuned scenarios on histopathology data, with a focus on interactive segmentation. Additionally, we compare SAM with other state-of-the-art interactive models to assess its practical potential and evaluate its generalization capability with domain adaptability. In the experimental results, SAM exhibits a weakness in segmentation performance compared to other models while demonstrating relative strengths in terms of inference time and generalization capability. To improve SAM's limited local refinement ability and to enhance prompt stability while preserving its core strengths, we propose a modification of SAM's decoder. the experimental results suggest that the proposed modification is effective to make SAM useful for interactive histology image segmentation. the code is available at https://***/hvcl/SAM_Interactive_Histopathology

关键词： Segment Anything Model Histopathology image Analysis Interactive Segmentation Foundation Models

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