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检索条件"任意字段=3rd International Conference on Medical Image Computing and Computer-Assisted Intervention"

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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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Automated CT Lung Cancer Screening Workflow Using 3D Camera 26th

Automated CT Lung Cancer Screening Workflow Using 3D Camera

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

作者： Teixeira, Brian Singh, Vivek Tamersoy, Birgi Prokein, Andreas Kapoor, Ankur Siemens Healthineers Digital Technol & Innovat Princeton NJ 08540 USA Siemens Healthineers Digital Technol & Innovat Erlangen Germany Siemens Healthineers Comp Tomog Forchheim Germany

ISBN: (纸本)9783031439896;9783031439902

Despite recent developments in CT planning that enabled automation in patient positioning, time-consuming scout scans are still needed to compute dose profile and ensure the patient is properly positioned. In this paper, we present a novel method which eliminates the need for scout scans in CT lung cancer screening by estimating patient scan range, isocenter, and Water Equivalent Diameter (WED) from 3D camera images. We achieve this task by training an implicit generative model on over 60,000 CT scans and introduce a novel approach for updating the prediction using real-time scan data. We demonstrate the effectiveness of our method on a testing set of 110 pairs of depth data and CT scan, resulting in an average error of 5 mm in estimating the isocenter, 13 mm in determining the scan range, 10 mm and 16 mm in estimating the AP and lateral WED respectively. The relative WED error of our method is 4%, which is well within the international Electrotechnical Commission (IEC) acceptance criteria of 10%.

关键词： CT Lung Screening Dose WED 3D Camera

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COLosSAL: A Benchmark for Cold-Start Active Learning for 3D medical image Segmentation 26th

COLosSAL: A Benchmark for Cold-Start Active Learning for 3D ...

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

作者： Liu, Han Li, Hao Yao, Xing Fan, Yubo Hu, Dewei Dawant, Benoit M. Nath, Vishwesh Xu, Zhoubing Oguz, Ipek Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA NVIDIA Nashville TN USA Siemens Healthineers Princeton NJ USA

ISBN: (纸本)9783031438943;9783031438950

medical image segmentation is a critical task in medical image analysis. In recent years, deep learning based approaches have shown exceptional performance when trained on a fully-annotated dataset. However, data annotation is often a significant bottleneck, especially for 3D medical images. Active learning (AL) is a promising solution for efficient annotation but requires an initial set of labeled samples to start active selection. When the entire data pool is unlabeled, how do we select the samples to annotate as our initial set? This is also known as the cold-start AL, which permits only one chance to request annotations from experts without access to previously annotated data. Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort. In this paper, we present a benchmark named COLosSAL by evaluating six cold-start AL strategies on five 3D medical image segmentation tasks from the public medical Segmentation Decathlon collection. We perform a thorough performance analysis and explore important open questions for cold-start AL, such as the impact of budget on different strategies. Our results show that cold-start AL is still an unsolved problem for 3D segmentation tasks but some important trends have been observed. The code repository, data partitions, and baseline results for the complete benchmark are publicly available at https://***/MedICL-VU/COLosSAL.

关键词： Efficient Annotation Active Learning Cold Start image Segmentation

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Concept Bottleneck with Visual Concept Filtering for Explainable medical image Classification 26th

Concept Bottleneck with Visual Concept Filtering for Explain...

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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, Injae Kim, Jongha Choi, Joonmyung Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9783031474002;9783031474019

Interpretability is a crucial factor in building reliable models for various medical applications. Concept Bottleneck Models (CBMs) enable interpretable image classification by utilizing human-understandable concepts as intermediate targets. Unlike conventional methods that require extensive human labor to construct the concept set, recent works leveraging Large Language Models (LLMs) for generating concepts made automatic concept generation possible. However, those methods do not consider whether a concept is visually relevant or not, which is an important factor in computing meaningful concept scores. Therefore, we propose a visual activation score that measures whether the concept contains visual cues or not, which can be easily computed with unlabeled image data. Computed visual activation scores are then used to filter out the less visible concepts, thus resulting in a final concept set with visually meaningful concepts. Our experimental results show that adopting the proposed visual activation score for concept filtering consistently boosts performance compared to the baseline. Moreover, qualitative analyses also validate that visually relevant concepts are successfully selected with the visual activation score.

关键词： medical image Classification Explainable AI Concept Bottleneck Models Large Language Models

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Anti-adversarial Consistency Regularization for Data Augmentation: Applications to Robust medical image Segmentation 26th

Anti-adversarial Consistency Regularization for Data Augment...

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

作者： Cho, Hyuna Han, Yubin Kim, Won Hwa Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (纸本)9783031439001;9783031439018

Modern deep learning methods for semantic segmentation require labor-intensive labeling for large-scale datasets with dense pixel-level annotations. Recent data augmentation methods such as dropping, mixing image patches, and adding random noises suggest effective ways to address the labeling issues for natural images. However, they can only be restrictively applied to medical image segmentation as they carry risks of distorting or ignoring the underlying clinical information of local regions of interest in an image. In this paper, we propose a novel data augmentation method for medical image segmentation without losing the semantics of the key objects (e.g., polyps). This is achieved by perturbing the objects with quasi-imperceptible adversarial noises and training a network to expand discriminative regions with a guide of anti-adversarial noises. Such guidance can be realized by a consistency regularization between the two contrasting data, and the strength of regularization is automatically and adaptively controlled considering their prediction uncertainty. Our proposed method significantly outperforms various existing methods with high sensitivity and Dice scores and extensive experiment results with multiple backbones on two datasets validate its effectiveness.

关键词： Adversarial attack and defense Data augmentation Semantic segmentation

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Implicit Anatomical Rendering for medical image Segmentation with Stochastic Experts 1

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

作者： You, Chenyu Dai, Weicheng Min, Yifei Staib, Lawrence Duncan, James S. Yale Univ Dept Elect Engn New Haven CT 06510 USA Yale Univ Dept Radiol & Biomed Imaging New Haven CT USA Yale Univ Dept Biomed Engn New Haven CT USA Yale Univ Dept Stat & Data Sci New Haven CT USA

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation. Towards this end, recent deep learning-based medical segmentation methods have shown great promise in better modeling such information. However, convolution operators for medical segmentation typically operate on regular grids, which inherently blur the high-frequency regions, i.e., boundary regions. In this work, we propose MORSE, a generic implicit neural rendering framework designed at an anatomical level to assist learning in medical image segmentation. Our method is motivated by the fact that implicit neural representation has been shown to be more effective in fitting complex signals and solving computer graphics problems than discrete grid-based representation. The core of our approach is to formulate medical image segmentation as a rendering problem in an end-to-end manner. Specifically, we continuously align the coarse segmentation prediction with the ambiguous coordinate-based point representations and aggregate these features to adaptively refine the boundary region. To parallelly optimize multi-scale pixel-level features, we leverage the idea from Mixture-of-Expert (MoE) to design and train our MORSE with a stochastic gating mechanism. Our experiments demonstrate that MORSE can work well with different medical segmentation backbones, consistently achieving competitive performance improvements in both 2D and 3D supervised medical segmentation methods. We also theoretically analyze the superiority of MORSE.

关键词： medical image Segmentation Implicit Neural Representation Stochastic Mixture-of-Experts

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Text-Guided Foundation Model Adaptation for Pathological image Classification 26th

Text-Guided Foundation Model Adaptation for Pathological Ima...

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

作者： Zhang, Yunkun Gao, Jin Zhou, Mu Wang, Xiaosong Qiao, Yu Zhang, Shaoting Wang, Dequan Shanghai Jiao Tong Univ Shanghai Peoples R China Rutgers State Univ Newark NJ USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9783031439032;9783031439049

The recent surge of foundation models in computer vision and natural language processing opens up perspectives in utilizing multi-modal clinical data to train large models with strong generalizability. Yet pathological image datasets often lack biomedical text annotation and enrichment. Guiding data-efficient image diagnosis from the use of biomedical text knowledge becomes a substantial interest. In this paper, we propose to Connect image and Text Embeddings (CITE) to enhance pathological image classification. CITE injects text insights gained from language models pre-trained with a broad range of biomedical texts, leading to adapt foundation models towards pathological image understanding. Through extensive experiments on the PatchGastric stomach tumor pathological image dataset, we demonstrate that CITE achieves leading performance compared with various baselines especially when training data is scarce. CITE offers insights into leveraging in-domain text knowledge to reinforce data-efficient pathological image classification. Code is available at https://***/Yunkun-Zhang/CITE.

关键词： Foundation models Multi-modality Model Adaptation Pathological image classification

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FSDiffReg: Feature-Wise and Score-Wise Diffusion-Guided Unsupervised Deformable image Registration for Cardiac images 26th

FSDiffReg: Feature-Wise and Score-Wise Diffusion-Guided Unsu...

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

作者： Qin, Yi Li, Xiaomeng Hong Kong Univ Sci & Technol Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Unsupervised deformable image registration is one of the challenging tasks in medical imaging. Obtaining a high-quality deformation field while preserving deformation topology remains demanding amid a series of deep-learning-based solutions. Meanwhile, the diffusion model's latent feature space shows potential in modeling the deformation semantics. To fully exploit the diffusion model's ability to guide the registration task, we present two modules: Feature-wise Diffusion-Guided Module (FDG) and Score-wise Diffusion-Guided Module (SDG). Specifically, FDG uses the diffusion model's multi-scale semantic features to guide the generation of the deformation field. SDG uses the diffusion score to guide the optimization process for preserving deformation topology with barely any additional computation. Experiment results on the 3D medical cardiac image registration task validate our model's ability to provide refined deformation fields with preserved topology effectively. Code is available at: https://***/xmed-lab/***.

关键词： Deformable image Registration Score-based Generative Model

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Deep Unsupervised Clustering for Conditional Identification of Subgroups Within a Digital Pathology image Set 26th

Deep Unsupervised Clustering for Conditional Identification ...

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

作者： Sidulova, Mariia Sun, Xudong Gossmann, Alexej US FDA Ctr Devices & Radiol Hlth Silver Spring MD 20993 USA Helmholtz Munich Inst AI Hlth Munich Germany George Washington Univ Dept Biomed Engn Washington DC USA

ISBN: (纸本)9783031439926;9783031439933

Consideration of subgroups or domains within medical image datasets is crucial for the development and evaluation of robust and generalizable machine learning systems. To tackle the domain identification problem, we examine deep unsupervised generative clustering approaches for representation learning and clustering. The Variational Deep Embedding (VaDE) model is trained to learn lower-dimensional representations of images based on a Mixture-of-Gaussians latent space prior distribution while optimizing cluster assignments. We propose the Conditionally Decoded Variational Deep Embedding (CDVaDE) model which incorporates additional variables of choice, such as the class labels, as conditioning factors to guide the clustering towards subgroup structures in the data which have not been known or recognized previously. We analyze the behavior of CDVaDE on multiple datasets and compare it to other deep clustering algorithms. Our experimental results demonstrate that the considered models are capable of separating digital pathology images into meaningful subgroups. We provide a general-purpose implementation of all considered deep clustering methods as part of the open source Python package DomId (https://***/DIDSR/DomId).

关键词： Domain Identification Deep Clustering Subgroup Identification Variational Autoencoder Generative Model

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HartleyMHA: Self-attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D image Segmentation 26th

HartleyMHA: Self-attention in Frequency Domain for Resolutio...

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

作者： Wong, Ken C. L. Wang, Hongzhi Syeda-Mahmood, Tanveer IBM Res Almaden Res Ctr San Jose CA 95120 USA

ISBN: (纸本)9783031439001;9783031439018

With the introduction of Transformers, different attention-based models have been proposed for image segmentation with promising results. Although self-attention allows capturing of long-range dependencies, it suffers from a quadratic complexity in the image size especially in 3D. To avoid the out-of-memory error during training, input size reduction is usually required for 3D segmentation, but the accuracy can be suboptimal when the trained models are applied on the original image size. To address this limitation, inspired by the Fourier neural operator (FNO), we introduce the HartleyMHA model which is robust to training image resolution with efficient self-attention. FNO is a deep learning framework for learning mappings between functions in partial differential equations, which has the appealing properties of zero-shot super-resolution and global receptive field. We modify the FNO by using the Hartley transform with shared parameters to reduce the model size by orders of magnitude, and this allows us to further apply self-attention in the frequency domain for more expressive high-order feature combination with improved efficiency. When tested on the BraTS'19 dataset, it achieved superior robustness to training image resolution than other tested models with less than 1% of their model parameters.

关键词： image segmentation Transformer Fourier neural operator Hartley transform Resolution-robust

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