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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351-360 订阅

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Multi-Target Domain Adaptation with Prompt Learning for medical image Segmentation 26th

Multi-Target Domain Adaptation with Prompt Learning for Medi...

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

作者： Lin, Yili Nie, Dong Liu, Yuting Yang, Ming Zhang, Daoqiang Wen, Xuyun Nanjing Univ Aeronaut & Astronaut Dept Comp Sci & Technol Nanjing Peoples R China Nanjing Med Univ Childrens Hosp Nanjing Peoples R China Alibaba Inc El Monte CA USA

ISBN: (纸本)9783031439063;9783031439070

Domain shift is a big challenge when deploying deep learning models in real-world applications due to various data distributions. the recent advances of domain adaptation mainly come from explicitly learning domain invariant features (e.g., by adversarial learning, metric learning and self-training). While they cannot be easily extended to multi-domains due to the diverse domain knowledge. In this paper, we present a novel multi-target domain adaptation (MTDA) algorithm, i.e., prompt-DA, through implicit feature adaptation for medical image segmentation. In particular, we build a feature transfer module by simply obtaining the domain-specific prompts and utilizing them to generate the domain-aware image features via a specially designed simple feature fusion module. Moreover, the proposed prompt-DA is compatible with the previous DA methods (e.g., adversarial learning based) and the performance can be continuously improved. the proposed method is evaluated on two challenging domain-shift datasets, i.e., the Iseg2019 (domain shift in infant MRI of different ages), and the BraTS2018 dataset (domain shift between high-grade and low-grade gliomas). Experimental results indicate our proposed method achieves state-of-the-art performance in both cases, and also demonstrates the effectiveness of the proposed prompt-DA. the experiments with adversarial learning DA show our proposed prompt-DA can go well with other DA methods. Our code is available at https://***/MurasakiLin/prompt-DA.

关键词： Domain Adaptation Prompt Learning Segmentation

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Enhancing IDC Histopathology image Classification: A Comparative Study of Fine-Tuned and Pre-trained Models 5th

Enhancing IDC Histopathology Image Classification: A Compara...

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5th international conference on Soft computing and its Engineering Applications (IcSoftComp)

作者： Kanadath, Anusree Jothi, J. Angel Arul Urolagin, Siddhaling Birla Inst Technol & Sci Pilani Dept Comp Sci Dubai Int Acad City Dubai Campus Dubai 345055 U Arab Emirates

ISBN: (纸本)9783031537271;9783031537288

Invasive ductal carcinoma (IDC) is a type of breast cancer that affects adult women all around the world. this cancer starts in the duct cells of the breast, spreads through the lymph system, and eventually affects nearby organs and bones. It's crucial for physicians to correctly identify the various forms of breast cancer. Instead of doing this manually, it's better to use computer programs because it saves time and reduces mistakes. this study presents a computer-assisted diagnosis method that utilizes deep convolutional neural networks to classify IDC histopathology images. these networks are trained using two forms of transfer learning: feature extraction and fine-tuning. In this study IDC classification is done using 4 well known deep learning networks, Xception, DenseNet169, ResNet101 and MobileNetV2. the dataset used is a publicly available IDC dataset containing 168 whole slide images. the evaluation results show that the fine-tuned models give better classification results than feature extractor models for IDC histopathology image classification.

关键词： Deep learning Transfer learning Histopathology image classification Fine-tuned models computer aided diagnosis

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Instructive Feature Enhancement for Dichotomous medical image Segmentation 26th

Instructive Feature Enhancement for Dichotomous Medical Imag...

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

作者： Liu, Lian Zhou, Han Chen, Jiongquan Liu, Sijing Shi, Wenlong Ni, Dong Fan, Deng-Ping Yang, Xin 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 Swiss Fed Inst Technol Comp Vis Lab CVL Zurich Switzerland

ISBN: (纸本)9783031439001;9783031439018

Deep neural networks have been widely applied in dichotomous medical image segmentation (DMIS) of many anatomical structures in several modalities, achieving promising performance. However, existing networks tend to struggle with task-specific, heavy and complex designs to improve accuracy. they made little instructions to which feature channels would be more beneficial for segmentation, and that may be why the performance and universality of these segmentation models are hindered. In this study, we propose an instructive feature enhancement approach, namely IFE, to adaptively select feature channels with rich texture cues and strong discriminability to enhance raw features based on local curvature or global information entropy criteria. Being plug-and-play and applicable for diverse DMIS tasks, IFE encourages the model to focus on texture-rich features which are especially important for the ambiguous and challenging boundary identification, simultaneously achieving simplicity, universality, and certain interpretability. To evaluate the proposed IFE, we constructed the first large-scale DMIS dataset Cosmos55k, which contains 55,023 images from 7 modalities and 26 anatomical structures. Extensive experiments show that IFE can improve the performance of classic segmentation networks across different anatomies and modalities with only slight modifications. Code is available at https://***/yezi-66/IFE.

关键词： Textures

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Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis 26th

Make-A-Volume: Leveraging Latent Diffusion Models for Cross-...

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

作者： Zhu, Lingting Xue, Zeyue Jin, Zhenchao Liu, Xian He, Jingzhen Liu, Ziwei Yu, Lequan Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shandong Univ Qilu Hosp Jinan Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9783031439988;9783031439995

Cross-modality medical image synthesis is a critical topic and has the potential to facilitate numerous applications in the medical imaging field. Despite recent successes in deep-learning-based generative models, most current medical image synthesis methods rely on generative adversarial networks and suffer from notorious mode collapse and unstable training. Moreover, the 2D backbone-driven approaches would easily result in volumetric inconsistency, while 3D backbones are challenging and impractical due to the tremendous memory cost and training difficulty. In this paper, we introduce a new paradigm for volumetric medical data synthesis by leveraging 2D backbones and present a diffusion-based framework, Make-A-Volume, for cross-modality 3D medical image synthesis. To learn the cross-modality slice-wise mapping, we employ a latent diffusion model and learn a low-dimensional latent space, resulting in high computational efficiency. To enable the 3D image synthesis and mitigate volumetric inconsistency, we further insert a series of volumetric layers in the 2D slice-mapping model and fine-tune them with paired 3D data. this paradigm extends the 2D image diffusion model to a volumetric version with a slightly increasing number of parameters and computation, offering a principled solution for generic cross-modality 3D medical image synthesis. We showcase the effectiveness of our Make-A-Volume framework on an in-house SWI-MRA brain MRI dataset and a public T1-T2 brain MRI dataset. Experimental results demonstrate that our framework achieves superior synthesis results with volumetric consistency.

关键词： Cross-modality medical image synthesis Volumetric data Latent diffusion model Brain MRI

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End-to-End Segmentation of medical images via Patch-Wise Polygons Prediction 25th

End-to-End Segmentation of Medical Images via Patch-Wise Pol...

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

作者： Shaharabany, Tal Wolf, Lior Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9783031164439;9783031164422

the leading medical image segmentation methods represent the output map as a pixel grid. We present an alternative in which the object edges are modeled, per image patch, as a polygon with k vertices that is coupled with per-patch label probabilities. the vertices are optimized by employing a differentiable neural renderer to create a raster image. the delineated region is then compared with the ground truth segmentation. Our method obtains multiple state-of-the-art results for the Gland segmentation dataset (Glas), the Nucleus challenges (MoNuSeg), and multiple polyp segmentation datasets, as well as for non-medical benchmarks, including Cityscapes, CUB, and Vaihingen. Our code for training and reproducing these results is attached as a supplement.

关键词： image segmentation

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Gradient and Feature Conformity-Steered medical image Classification with Noisy Labels 26th

Gradient and Feature Conformity-Steered Medical Image Classi...

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

作者： Xing, Xiaohan Chen, Zhen Gao, Zhifan Yuan, Yixuan Stanford Univ Dept Radiat Oncol Stanford CA USA Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot CAIR Hong Kong Peoples R China Sun Yat Sen Univ Sch Biomed Engn Guangzhou Guangdong Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Noisy annotations are inevitable in clinical practice due to the requirement of labeling efforts and expert domain knowledge. therefore, medical image classification with noisy labels is an important topic. A recently advanced paradigm in learning with noisy labels (LNL) first selects clean data with small-loss criterion, then formulates the LNL problem as semi-supervised learning (SSL) task and employs Mixup to augment the dataset. However, the small-loss criterion is vulnerable to noisy labels and the Mixup operation is prone to accumulate errors in pseudo labels. To tackle these issues, we present a two-stage framework with novel criteria for clean data selection and a more advanced Mixup method for SSL. In the clean data selection stage, based on the observation that gradient space reflects optimization dynamics and feature space is more robust to noisy labels, we propose two novel criteria, i.e., Gradient Conformity-based Selection (GCS) and Feature Conformity-based Selection (FCS), to select clean samples. Specifically, the GCS and FCS criteria identify clean data that better aligns with the class-wise optimization dynamics in the gradient space and principal eigenvector in the feature space. In the SSL stage, to effectively augment the dataset while mitigating disturbance of unreliable pseudo-labels, we propose a Sample Reliability-based Mixup (SRMix) method which selects mixup partners based on their spatial reliability, temporal stability, and prediction confidence. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods on two medical datasets with synthetic and real-world label noise. the code is available at https://***/hathawayxxh/FGCS-LNL.

关键词： Label noise Gradient conformity Feature eigenvector conformity Mixup

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Position-Aware Masked Autoencoder for Histopathology WSI Representation Learning 26th

Position-Aware Masked Autoencoder for Histopathology WSI Rep...

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

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

ISBN: (纸本)9783031439865;9783031439872

Transformer-based multiple instance learning (MIL) framework has been proven advanced for whole slide image (WSI) analysis. However, existing spatial embedding strategies in Transformer can only represent fixed structural information, which are hard to tackle the scale-varying and isotropic characteristics of WSIs. Moreover, the current MIL cannot take advantage of a large number of unlabeled WSIs for training. In this paper, we propose a novel self-supervised whole slide image representation learning framework named position-aware masked autoencoder (PAMA), which can make full use of abundant unlabeled WSIs to improve the discrimination of slide features. Moreover, we propose a position-aware cross-attention (PACA) module with a kernel reorientation (KRO) strategy, which makes PAMA able to maintain spatial integrity and semantic enrichment during the training. We evaluated the proposed method on a public TCGA-Lung dataset with 3,064 WSIs and an in-house Endometrial dataset with 3,654 WSIs, and compared it with 6 state-of-the-art methods. the results of experiments show our PAMA is superior to SOTA MIL methods and SSL methods. the code will be available at https://***/WkEEn/PAMA.

关键词： WSI representation learning Self-supervised learning

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Context-Aware Pseudo-label Refinement for Source-Free Domain Adaptive Fundus image Segmentation 26th

Context-Aware Pseudo-label Refinement for Source-Free Domain...

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

作者： Huai, Zheang Ding, Xinpeng Li, Yi Li, Xiaomeng Hong Kong Univ Sci & Technol Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783031439896;9783031439902

In the domain adaptation problem, source data may be unavailable to the target client side due to privacy or intellectual property issues. Source-free unsupervised domain adaptation (SF-UDA) aims at adapting a model trained on the source side to align the target distribution with only the source model and unlabeled target data. the source model usually produces noisy and context-inconsistent pseudo-labels on the target domain, i.e., neighbouring regions that have a similar visual appearance are annotated with different pseudo-labels. this observation motivates us to refine pseudo-labels with context relations. Another observation is that features of the same class tend to form a cluster despite the domain gap, which implies context relations can be readily calculated from feature distances. To this end, we propose a context-aware pseudo-label refinement method for SF-UDA. Specifically, a context-similarity learning module is developed to learn context relations. Next, pseudo-label revision is designed utilizing the learned context relations. Further, we propose calibrating the revised pseudo-labels to compensate for wrong revision caused by inaccurate context relations. Additionally, we adopt a pixel-level and class-level denoising scheme to select reliable pseudo-labels for domain adaptation. Experiments on cross-domain fundus images indicate that our approach yields the state-of-the-art results. Code is available at https://***/xmed-lab/CPR.

关键词： Source-free domain adaptation Context similarity Pseudo-label refinement Fundus image

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Head CT Scan Motion Artifact Correction via Diffusion-Based Generative Models 3rd

Head CT Scan Motion Artifact Correction via Diffusion-Based ...

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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： Chen, Zhennong Yoon, Siyeop Strotzer, Quirin Khalid, Rehab Naeem Tivnan, Matthew Li, Quanzheng Gupta, Rajiv Wu, Dufan Center for Advanced Medical Computing and Analysis Massachusetts General Hospital and Harvard Medical School BostonMA02114 United States Department of Radiology Massachusetts General Hospital and Harvard Medical School BostonMA02114 United States

ISBN: (纸本)9783031820069

Head motion is a major source of image artifacts in head computed tomography (CT), degrading the image quality and impacting diagnosis. image-domain-based motion correction is practical for routine use since it doesn’t rely on hard-to-obtain CT projection data. However, existing convolutional neural network (CNN)-based methods tend to over-smooth images, particularly in cases of moderate to severe 3D motion artifacts. Motivated by the improved image quality and more stable training of diffusion-based generative models, we propose a novel 3D head CT motion correction approach based on conditional diffusion, named HeadMotion-EDM (HM-EDM). this approach has three features. Firstly, we utilize motion-corrupted images as the conditional input. Secondly, we leverage the advanced Elucidated Diffusion Model (EDM) framework, which integrates several pivotal engineering improvements in the diffusion model and significantly expedites the sampling process. thirdly, we design an efficient 3D-patch-wise training method for 3D CT data. Comparative studies demonstrate that our approach surpasses CNN-based techniques as well as the denoising diffusion probabilistic model (DDPM) in both simulation and phantom studies. Furthermore, radiologists reviewed the results of applying HM-EDM to real-world portable head CT scans, showing its effectiveness in eliminating motion artifacts and improving diagnostic value. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning 26th

Encoding Surgical Videos as Latent Spatiotemporal Graphs for...

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

作者： Murali, Aditya Alapatt, Deepak Mascagni, Pietro Vardazaryan, Armine Garcia, Alain Okamoto, Nariaki Mutter, Didier Padoy, Nicolas Univ Strasbourg CNRS ICube Strasbourg France IHU Strasbourg Inst Image Guided Surg Strasbourg France Fdn Policlin Univ Agostino Gemelli IRCCS Rome Italy Inst Res Digest Canc IRCAD Strasbourg France

ISBN: (纸本)9783031439957;9783031439964

Recently, spatiotemporal graphs have emerged as a concise and elegant manner of representing video clips in an object-centric fashion, and have shown to be useful for downstream tasks such as action recognition. In this work, we investigate the use of latent spatiotemporal graphs to represent a surgical video in terms of the constituent anatomical structures and tools and their evolving properties over time. To build the graphs, we first predict frame-wise graphs using a pre-trained model, then add temporal edges between nodes based on spatial coherence and visual and semantic similarity. Unlike previous approaches, we incorporate long-term temporal edges in our graphs to better model the evolution of the surgical scene and increase robustness to temporary occlusions. We also introduce a novel graph-editing module that incorporates prior knowledge and temporal coherence to correct errors in the graph, enabling improved downstream task performance. Using our graph representations, we evaluate two downstream tasks, critical view of safety prediction and surgical phase recognition, obtaining strong results that demonstrate the quality and flexibility of the learned representations. Code is available at ***/CAMMA-public/SurgLatentGraph.

关键词： Scene Graphs Surgical Scene Understanding Representation Learning

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