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检索条件"任意字段=6th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 3012 条记录，以下是181-190 订阅

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PMC-CLIP: Contrastive Language-image Pre-training Using Biomedical Documents 26th

PMC-CLIP: Contrastive Language-Image Pre-training Using Biom...

引用

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

作者： Lin, Weixiong Zhao, Ziheng Zhang, Xiaoman Wu, Chaoyi Zhang, Ya Wang, Yanfeng Xie, Weidi Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9783031439926;9783031439933

Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity. To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral's OpenAccess subset, which is 8 times larger than before, PMC-OA covers diverse modalities or diseases, with majority of the image-caption samples aligned at finer-grained level, i.e., subfigure and subcaption. While pretraining a CLIP-style model on PMC-OA, our model named PMC-CLIP outperform previous state-of-the-art models on various downstream tasks, including image-text retrieval on ROCO, MedMNIST image classification, medical VQA, for example, +8.1% R@10 on image-text retrieval, +3.9% accuracy on image classification.

关键词： Multimodal Dataset Vision-Language Pretraining

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Self-adaptive Adversarial Training for Robust medical Segmentation 1

引用

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

作者： Wang, Fu Fu, Zeyu Zhang, Yanghao Ruan, Wenjie Univ Exeter Exeter EX4 4QF Devon England Univ Liverpool Liverpool L69 3BX Merseyside England

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Adversarial training has been demonstrated to be one of the most effective approaches to training deep neural networks that are robust to malicious perturbations. Research on effectively applying it to produce robust 3D medical image segmentation models is ongoing. While few empirical studies have been done in this area, developing effective adversarial training methods for complex segmentation models and high-volume 3D examples is challenging and requires theoretical support. In this paper, we consider the robustness of 3D segmentation tasks from a PAC-Bayes generalisation perceptive and show that reducing the trained models' Lipschitz constant benefits the models' robustness performance. Demonstrating by empirical investigation, we show that adjusting the adversarial iteration can help to reduce the model's Lipschitz constant, enabling a self-adaptive adversarial training strategy. Empirical studies on the medical segmentation decathlon dataset have been done to demonstrate the efficiency of the proposed adversarial training method. Our implementation is available at https://***/TrustAI/SEAT.

关键词： medical image Segmentation Adversarial Training

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GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as a Plug-and-Play Transductive Model for medical image Analysis 26th

GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3...

引用

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： Zhang, Yizhe Chen, Danny Z. Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9783031474002;9783031474019

In this paper, we propose a novel approach (called GPT4MIA) that utilizes Generative Pre-trained Transformer (GPT) as a plug-and-play transductive inference tool for medical image analysis (MIA). We provide theoretical analysis on why a large pre-trained language model such as GPT-3 can be used as a plug-and-play transductive inference model for MIA. At the methodological level, we develop several technical treatments to improve the efficiency and effectiveness of GPT4MIA, including better prompt structure design, sample selection, and prompt ordering of representative samples/features. We present two concrete use cases (with workflow) of GPT4MIA: (1) detecting prediction errors and (2) improving prediction accuracy, working in conjecture with well-established vision-based models for image classification (e.g., ResNet). Experiments validate that our proposed method is effective for these two tasks. We further discuss the opportunities and challenges in utilizing Transformer-based large language models for broader MIA applications.

关键词： medical image Classification Generative Pre-trained Transformer GPT-3 Large Language Models Transductive Inference

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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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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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Multi-scale Prototypical Transformer forWhole Slide image Classification 26th

Multi-scale Prototypical Transformer forWhole Slide Image Cl...

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

作者： Ding, Saisai Wang, Jun Li, Juncheng Shi, Jun Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Whole slide image (WSI) classification is an essential task in computational pathology. Despite the recent advances in multiple instance learning (MIL) for WSI classification, accurate classification of WSIs remains challenging due to the extreme imbalance between the positive and negative instances in bags, and the complicated pre-processing to fuse multi-scale information ofWSI. To this end, we propose a novel multi-scale prototypical Transformer (MSPT) for WSI classification, which includes a prototypical Transformer (PT) module and a multi-scale feature fusion module (MFFM). the PT is developed to reduce redundant instances in bags by integrating prototypical learning into the Transformer architecture. It substitutes all instances with cluster prototypes, which are then re-calibrated through the self-attention mechanism of Transformer. thereafter, an MFFM is proposed to fuse the clustered prototypes of different scales, which employs MLP-Mixer to enhance the information communication between prototypes. the experimental results on two public WSI datasets demonstrate that the pro-posed MSPT outperforms all the compared algorithms, suggesting its potential applications.

关键词： Whole slide image Multiple instance learning Multi-scale feature Prototypical Transformer

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NASDM: Nuclei-Aware Semantic Histopathology image Generation Using Diffusion Models 26th

NASDM: Nuclei-Aware Semantic Histopathology Image Generation...

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

作者： Shrivastava, Aman Fletcher, P. thomas Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9783031439865;9783031439872

In recent years, computational pathology has seen tremendous progress driven by deep learning methods in segmentation and classification tasks aiding prognostic and diagnostic settings. Nuclei segmentation, for instance, is an important task for diagnosing different cancers. However, training deep learning models for nuclei segmentation requires large amounts of annotated data, which is expensive to collect and label. this necessitates explorations into generative modeling of histopathological images. In this work, we use recent advances in conditional diffusion modeling to formulate a first-of-its-kind nuclei-aware semantic tissue generation framework (NASDM) which can synthesize realistic tissue samples given a semantic instance mask of up to six different nuclei types, enabling pixel-perfect nuclei localization in generated samples. these synthetic images are useful in applications in pathology pedagogy, validation of models, and supplementation of existing nuclei segmentation datasets. We demonstrate that NASDM is able to synthesize high-quality histopathology images of the colon with superior quality and semantic controllability over existing generative methods. Implementation: https://***/4m4n5/NASDM.

关键词： Generative Modeling Histopathology Diffusion Models

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Conditional Diffusion Models for Weakly Supervised medical image Segmentation 26th

Conditional Diffusion Models for Weakly Supervised Medical I...

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

作者： Hu, Xinrong Chen, Yu-Jen Ho, Tsung-Yi Shi, Yiyu Univ Notre Dame Notre Dame IN 46556 USA Natl Tsing Hua Univ Hsinchu Taiwan Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks. While there are already works exploring the potential of this powerful tool in image semantic segmentation, its application in weakly supervised semantic segmentation (WSSS) remains relatively under-explored. Observing that conditional diffusion models (CDM) is capable of generating images subject to specific distributions, in this work, we utilize category-aware semantic information underlied in CDM to get the prediction mask of the target object with only image-level annotations. More specifically, we locate the desired class by approximating the derivative of the output of CDM w.r.t the input condition. Our method is different from previous diffusion model methods with guidance from an external classifier, which accumulates noises in the background during the reconstruction process. Our method outperforms state-of-the-art CAM and diffusion model methods on two public medical image segmentation datasets, which demonstrates that CDM is a promising tool in WSSS. Also, experiment shows our method is more time-efficient than existing diffusion model methods, making it practical for wider applications. the codes are available at https://***/xhu248/cond_ddpm_wsss.

关键词： weakly supervised semantic segmentation diffusion models brain tumor magnetic resonance imaging

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Optimization of Convolutional Neural Network Architecture by PSO Algorithm for MRI Brain Tumor image Classification 6

Optimization of Convolutional Neural Network Architecture by...

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6th international conference on Intelligent computing in Data Sciences, ICDS 2024

作者： Amoury, Sofia El Smili, Youssef Fakhri, Youssef Hassan 1 University ENIC FSTS Settat Morocco Hassan 1 University Department of Mathematics and Computer Science FSTS Settat Morocco RI Ibn Toufail University Kenitra Morocco

ISBN: (纸本)9798350351200

In recent years, the application of convolutional neural networks (CNN) has significantly advanced image clas-sification tasks, particularly in the medical field. In the present study, we applied CNN to magnetic resonance imaging (MRI) to classify brain tumors. Building a CNN requires adjustment of configuration architecture. For such adjustment, we used Particle Swarm Optimization (PSO) algorithm for optimal configuration that allow us high accuracy. Experimental results showed that PSO applied to CNN performed with a classification accuracy of 98%. © 2024 IEEE.

关键词： Convolutional neural networks

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SENDD: Sparse Efficient Neural Depth and Deformation for Tissue Tracking 26th

SENDD: Sparse Efficient Neural Depth and Deformation for Tis...

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

作者： Schmidt, Adam Mohareri, Omid DiMaio, Simon Salcudean, Septimiu E. Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Adv Res Intuit Surg Sunnyvale CA 94086 USA

ISBN: (纸本)9783031439957;9783031439964

Deformable tracking and real-time estimation of 3D tissue motion is essential to enable automation and image guidance applications in robotically assisted surgery. Our model, Sparse Efficient Neural Depth and Deformation (SENDD), extends prior 2D tracking work to estimate flow in 3D space. SENDD introduces novel contributions of learned detection, and sparse per-point depth and 3D flow estimation, all with less than half a million parameters. SENDD does this by using graph neural networks of sparse keypoint matches to estimate both depth and 3D flow anywhere. We quantify and benchmark SENDD on a comprehensively labelled tissue dataset, and compare it to an equivalent 2D flow model. SENDD performs comparably while enabling applications that 2D flow cannot. SENDD can track points and estimate depth at 10fps on an NVIDIA RTX 4000 for 1280 tracked (query) points and its cost scales linearly with an increasing/decreasing number of points. SENDD enables multiple downstream applications that require estimation of 3D motion in stereo endoscopy.

关键词： Tissue tracking Graph neural networks Scene flow

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