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

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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...

引用

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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Input Augmentation with SAM: Boosting medical image Segmentation with Segmentation Foundation Model 26th

Input Augmentation with SAM: Boosting Medical Image Segmenta...

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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： Zhang, Yizhe Zhou, Tao Wang, Shuo Liang, Peixian Zhang, Yejia Chen, Danny Z. Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Fudan Univ Sch Basic Med Sci Digital Med Res Ctr Shanghai 200032 Peoples R China Shanghai Key Lab MICCAI Shanghai 200032 Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9783031474002;9783031474019

the Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. SAM can be viewed as a general perception model for segmentation (partitioning images into semantically meaningful regions). thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target. this paper shows that although SAM does not immediately give high-quality segmentation for medical image data, its generated masks, features, and stability scores are useful for building and training better medical image segmentation models. In particular, we demonstrate how to use SAM to augment image input for commonly-used medical image segmentation models (e.g., U-Net). Experiments on three segmentation tasks show the effectiveness of our proposed SAMAug method.

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Intraoperative CT Augmentation for Needle-Based Liver interventions 26th

Intraoperative CT Augmentation for Needle-Based Liver Interv...

引用

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

作者： El Hadramy, Sidaty Verde, Juan Padoy, Nicolas Cotin, Stephane INRIA Strasbourg France Univ Strasbourg ICube CNRS Strasbourg France IHU Strasbourg Strasbourg France

ISBN: (纸本)9783031439957;9783031439964

this paper addresses the need for improved CT-guidance during needle-based liver procedures (i.e., tumor ablation), while reduces the need for contrast agent injection during such interventions. To achieve this objective, we augment the intraoperative CT with the preoperative vascular network deformed to match the current acquisition. First, a neural network learns local image features in a non-contrasted CT image by leveraging the known preoperative vessel tree geometry and topology extracted from a matching contrasted CT image. then, the augmented CT is generated by fusing the labeled vascular tree and the non-contrasted intraoperative CT. Our method is trained and validated on porcine data, achieving an average dice score of 0.81 on the predicted vessel tree instead of 0.51 when a medical expert segments the non-contrasted CT. In addition, vascular labels can also be transferred to provide additional information. Source code of this work is publicly available at https://***/Sidaty1/Intraoperative CT augmentation.

关键词： Liver tumor ablation Needle-based procedures Patient-specific interventions CT-guidance medical image augmentation

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Boundary Difference over Union Loss for medical image Segmentation 26th

Boundary Difference over Union Loss for Medical Image Segmen...

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

作者： Sun, Fan Luo, Zhiming Li, Shaozi Xiamen Univ Dept Artificial Intelligence Xiamen Fujian Peoples R China

ISBN: (纸本)9783031439001;9783031439018

medical image segmentation is crucial for clinical diagnosis. However, current losses for medical image segmentation mainly focus on overall segmentation results, with fewer losses proposed to guide boundary segmentation. those that do exist often need to be used in combination with other losses and produce ineffective results. To address this issue, we have developed a simple and effective loss called the Boundary Difference over Union Loss (Boundary DoU Loss) to guide boundary region segmentation. It is obtained by calculating the ratio of the difference set of prediction and ground truth to the union of the difference set and the partial intersection set. Our loss only relies on region calculation, making it easy to implement and training stable without needing any additional losses. Additionally, we use the target size to adaptively adjust attention applied to the boundary regions. Experimental results using UNet, TransUNet, and Swin-UNet on two datasets (ACDC and Synapse) demonstrate the effectiveness of our proposed loss function. Code is available at https://***/sunfan-bvb/BoundaryDoULoss.

关键词： medical image segmentation Boundary loss

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Histopathological image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization 26th

Histopathological Image Analysis with Style-Augmented Featur...

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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： Khamankar, Vaibhav Bera, Sutanu Bhattacharya, Saumik Sen, Debashis Biswas, Prabir Kumar Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur India

ISBN: (纸本)9783031474002;9783031474019

Histopathological images are essential for medical diagnosis and treatment planning, but interpreting them accurately using machine learning can be challenging due to variations in tissue preparation, staining and imaging protocols. Domain generalization aims to address such limitations by enabling the learning models to generalize to new datasets or populations. Style transfer-based data augmentation is an emerging technique that can be used to improve the generalizability of machine learning models for histopathological images. However, existing style transfer-based methods can be computationally expensive, and they rely on artistic styles, which may negatively impact model accuracy. In this study, we propose a feature domain style mixing technique that uses adaptive instance normalization to estimate style-mixed versions of image features. We compare our proposed method with existing style transfer-based data augmentation methods and found that it performs similarly or better, despite requiring lower computation. Our results demonstrate the potential of feature domain statistics mixing in the generalization of learning models for histopathological image analysis.

关键词： Domain Shift Domain Generalization Mitotic Figure Style Mixing Feature Domain Augmentation Histopathological image

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Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in medical image Segmentation 26th

Learning Transferable Object-Centric Diffeomorphic Transform...

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

作者： Kumar, Nilesh Gyawali, Prashnna K. Ghimire, Sandesh Wang, Linwei Rochester Inst Technol Rochester NY 14623 USA West Virginia Univ Morgantown WV 26506 USA Qualcomm Inc San Diego CA USA

ISBN: (纸本)9783031438943;9783031438950

Obtaining labelled data in medical image segmentation is challenging due to the need for pixel-level annotations by experts. Recent works have shown that augmenting the object of interest with deformable transformations can help mitigate this challenge. However, these transformations have been learned globally for the image, limiting their transferability across datasets or applicability in problems where image alignment is difficult. While object-centric augmentations provide a great opportunity to overcome these issues, existing works are only focused on position and random transformations without considering shape variations of the objects. To this end, we propose a novel object-centric data augmentation model that is able to learn the shape variations for the objects of interest and augment the object in place without modifying the rest of the image. We demonstrated its effectiveness in improving kidney tumour segmentation when leveraging shape variations learned both from within the same dataset and transferred from external datasets.

关键词： Data Augmentation Diffeomorphic transformations image Segmentation

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ULung:A Novel Approach for Lung image Segmentation 6

ULung:A Novel Approach for Lung Image Segmentation

引用

6th international conference on computing and Informatics, ICCI 2024

作者： Pal, Osim Kumar Roy, Shuvasish Modok, Anup Kumar Teethi, Tajmihir Islam Sarker, Siddhartha Kumer Dept. of Biomedical Engineering University of Kragujevac Kragujevac34000 Serbia Dept. of Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Daffodil International University Ashulia Savar Bangladesh Dept. of Space Communication and Navigation Technology BSMRAAU Old Airport Dhaka Bangladesh

ISBN: (纸本)9798350373875

ULung is an innovative medical technology used for segmenting medical images in pulmonary diagnostics. It introduces a modified approach to analyzing and understanding pulmonary conditions. ULung employs cutting-edge deep learning and image processing techniques to isolate intricate characteristics in medical lung images properly. the model uses advanced convolutional neural network architecture as UNet layer stricture to detect abnormalities and delineate anatomical regions reliably. ULung exhibits versatility in handling various lung ailments after extensive training using diverse datasets. the innovative methodology ensures enhanced segmentation performance, enhancing diagnostic precision and expediting medical evaluations. the advent of ULung has greatly enhanced medical imaging, providing clinicians with a potent tool for precise and expeditious lung evaluation. ULung is an impressive advancement in medical image segmentation that can revolutionize standards in respiratory healthcare due to its robustness and adaptability. the model's accuracy achieved using EfficientNetV2 is 99 %, while its precision and recall rates are 98% and 96 %, respectively. the model achieves a Mean Intersection over Union (MeanloU) of 91.48% after the 14th epoch. © 2024 IEEE.

关键词： image segmentation

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Text-Guided Cross-Position Attention for Segmentation: Case of medical image 26th

Text-Guided Cross-Position Attention for Segmentation: Case ...

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

作者： Lee, Go-Eun Kim, Seon Ho Cho, Jungchan Choi, Sang Tae Choi, Sang-Il Dankook Univ Yongin Gyeonggi Do South Korea Univ Southern Calif Los Angeles CA 90007 USA Gachon Univ Seongnam Gyeonggi Do South Korea Chung Ang Univ Coll Med Seoul South Korea

ISBN: (纸本)9783031439032;9783031439049

We propose a novel text-guided cross-position attention module which aims at applying a multi-modality of text and image to position attention in medical image segmentation. To match the dimension of the text feature to that of the image feature map, we multiply learnable parameters by text features and combine the multi-modal semantics via cross-attention. It allows a model to learn the dependency between various characteristics of text and image. Our proposed model demonstrates superior performance compared to other medical models using image-only data or image-text data. Furthermore, we utilize our module as a region of interest (RoI) generator to classify the inflammation of the sacroiliac joints. the RoIs obtained from the model contribute to improve the performance of classification models.

关键词： image Segmentation Multi Modal Learning Cross Position Attention Text-Guided Attention medical image

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Pre-trained Diffusion Models for Plug-and-Play medical image Enhancement 1

引用

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

作者： Ma, Jun Zhu, Yuanzhi You, Chenyu Wang, Bo Univ Hlth Network Peter Munk Cardiac Ctr Toronto ON Canada Univ Toronto Dept Lab Med & Pathobiol Toronto ON Canada Vector Inst Artificial Intelligence Toronto ON Canada Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland Yale Univ Dept Elect Engn New Haven CT USA Univ Toronto Dept Comp Sci Toronto ON Canada Univ Hlth Network AI Hub Toronto ON Canada

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Deep learning-based medical image enhancement methods (e.g., denoising and super-resolution) mainly rely on paired data and correspondingly the well-trained models can only handle one type of task. In this paper, we address the limitation with a diffusion model-based framework that mitigates the requirement of paired data and can simultaneously handle multiple enhancement tasks by one pre-trained diffusion model without fine-tuning. Experiments on low-dose CT and heart MR datasets demonstrate that the proposed method is versatile and robust for image denoising and super-resolution. We believe our work constitutes a practical and versatile solution to scalable and generalizable image enhancement.

关键词： image enhancement

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GPC: Generative and General Pathology image Classifier 26th

GPC: Generative and General Pathology Image Classifier

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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： Nguyen, Anh Tien Kwak, Jin Tae Korea Univ Sch Elect Engn Seoul 02841 South Korea

ISBN: (纸本)9783031474002;9783031474019

Deep learning has been increasingly incorporated into various computational pathology applications to improve its efficiency, accuracy, and robustness. Although successful, most previous approaches for image classification have crucial drawbacks. there exist numerous tasks in pathology, but one needs to build a model per task, i.e., a task-specific model, thereby increasing the number of models, training resources, and cost. Moreover, transferring arbitrary task-specific model to another task is still a challenging problem. Herein, we propose a task-agnostic generative and general pathology image classifier, so called GPC, that aims at learning from diverse kinds of pathology images and conducting numerous classification tasks in a unified model. GPC, equipped with a convolutional neural network and a Transformer-based language model, maps pathology images into a high-dimensional feature space and generates pertinent class labels as texts via the image-to-text classification mechanism. We evaluate GPC on six datasets for four different pathology image classification tasks. Experimental results show that GPC holds considerable potential for developing an effective and efficient universal model for pathology image analysis.

关键词： Computational pathology image classification Generative model image-to-Text

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