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

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AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic medical image Matching 25th

AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Ma...

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

作者： Nguyen, Khanh Nguyen, Huy Hoang Tiulpin, Aleksei Univ Oulu Oulu Finland

ISBN: (纸本)9783031164521;9783031164514

this paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). the main challenge in FMIM compared to the general case of CBIR, is that the subject to whom a query image belongs may be affected by aging and progressive degenerative disorders, making it difficult to match data on a subject level. CBIR with DNNs is generally solved by minimizing a ranking loss, such as Triplet loss (TL), computed on image representations extracted by a DNN from the original data. TL, in particular, operates on triplets: anchor, positive (similar to anchor) and negative (dissimilar to anchor). Although TL has been shown to perform well in many CBIR tasks, it still has limitations, which we identify and analyze in this work. In this paper, we introduce (i) the AdaTriplet loss - an extension of TL whose gradients adapt to different difficulty levels of negative samples, and (ii) the AutoMargin method - a technique to adjust hyperparameters of margin-based losses such as TL and our proposed loss dynamically. Our results are evaluated on two large-scale benchmarks for FMIM based on the Osteoarthritis Initiative and Chest X-ray-14 datasets. the codes allowing replication of this study have been made publicly available at https://***/Oulu-IMEDS/AdaTriplet.

关键词： Deep learning Content-based image retrieval Forensic matching

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SETMIL: Spatial Encoding Transformer-Based Multiple Instance Learning for Pathological image Analysis 25th

SETMIL: Spatial Encoding Transformer-Based Multiple Instance...

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

作者： Zhao, Yu Lin, Zhenyu Sun, Kai Zhang, Yidan Huang, Junzhou Wang, Liansheng Yao, Jianhua Xiamen Univ Sch Informat Dept Comp Sci Xiamen 361005 Peoples R China Al Lab Tencent Shenzhen 518000 Peoples R China Cent South Univ Sch Basic Med Sci Changsha 410013 Peoples R China Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Considering the huge size of the gigapixel whole slide image (WSI), multiple instance learning (MIL) is normally employed to address pathological image analysis tasks, where learning an informative and effective representation of each WSI plays a central role but remains challenging due to the weakly supervised nature of MIL. To this end, we present a novel Spatial Encoding Transformer-based MIL method, SETMIL, which has the following advantages. (1) It is a typical embedded-space MIL method and therefore has the advantage of generating the bag embedding by comprehensively encoding all instances with a fully trainable transformer-based aggregating module. (2) SETMIL leverages spatial-encoding-transformer layers to update the representation of an instance by aggregating both neighbouring instances and globally-correlated instances simultaneously. (3) the joint absolute-relative position encoding design in the aggregating module further improves the context-information-encoding ability of SETMIL. (4) SETMIL designs a transformer-based pyramid multi-scale fusion module to comprehensively encode the information with different granularity using multi-scale receptive fields and make the obtained representation enriched with multi-scale context information. Extensive experiments demonstrated the superior performance of SETMIL in challenging pathological image analysis tasks such as gene mutation and lymph node metastasis prediction.

关键词： Multiple instance learning Pathological image analysis Transformer Position encoding

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Less is More: Adaptive Curriculum Learning for thyroid Nodule Diagnosis 1

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

作者： Gong, Haifan Cheng, Hui Xie, Yifan Tan, Shuangyi Chen, Guanqi Chen, Fei Li, Guanbin Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China Southern Med Univ Zhujiang Hosp Guangzhou Guangdong Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

thyroid nodule classification aims at determining whether the nodule is benign or malignant based on a given ultrasound image. However, the label obtained by the cytological biopsy which is the golden standard in clinical medicine is not always consistent with the ultrasound imaging TI-RADS criteria. the information difference between the two causes the existing deep learning-based classification methods to be indecisive. To solve the Inconsistent Label problem, we propose an Adaptive Curriculum Learning (ACL) framework, which adaptively discovers and discards the samples with inconsistent labels. Specifically, ACL takes both hard sample and model certainty into account, and could accurately determine the threshold to distinguish the samples with Inconsistent Label. Moreover, we contribute TNCD: a thyroid Nodule Classification Dataset to facilitate future related research on the thyroid nodules. Extensive experimental results on TNCD based on three different backbone networks not only demonstrate the superiority of our method but also prove that the less-is-more principle which strategically discards the samples with Inconsistent Label could yield performance gains. Source code and data are available at https://***/chenghui-666/ACL/.

关键词： thyroid nodule Adaptive curriculum learning Ultrasound imaging image classification

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Spatial-Hierarchical Graph Neural Network with Dynamic Structure Learning for Histological image Classification 25th

Spatial-Hierarchical Graph Neural Network with Dynamic Struc...

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

作者： Hou, Wentai Huang, Helong Peng, Qiong Yu, Rongshan Yu, Lequan Wang, Liansheng Xiamen Univ Sch Informat Informat & Commun Engn Dept Xiamen Peoples R China Xiamen Univ Sch Informat Dept Comp Sci Xiamen Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Pok Fu Lam Hong Kong Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Graph neural network (GNN) has achieved tremendous success in histological image classification, as it can explicitly model the notion and interaction of different biological entities (e.g., cell, tissue and etc.). However, the potential of GNN has not been fully unleashed for histological image analysis due to (1) the fixed design mode of graph structure and (2) the insufficient interactions between multi-level entities. In this paper, we proposed a novel spatial-hierarchical GNN framework (SHGNN) equipped with a dynamic structure learning (DSL) module for effective histological image classification. Compared with traditional GNNs, the proposed framework has two compelling characteristics. First, the DSL module integrates the positional attribute and semantic representation of entities to learn the adjacency relationship of them during the training process. Second, the proposed SHGNN can extract rich and discriminative features by mining the spatial features of different entities via graph convolutions and aggregating the semantic of multi-level entities via a vision transformer (ViT) based interaction mechanism. We evaluate the proposed framework on our collected colorectal cancer staging (CRCS) dataset and the public breast carcinoma subtyping (BRAGS) dataset. Experimental results demonstrate that our proposed method yield superior classification results compared to state-of-the-arts.

关键词： Histological image classification Graph neural network Dynamic structure Hierarchical representation Vision transformer

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Classification of Myopic Maculopathy images with Self-supervised Driven Multiple Instance Learning Network 1

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Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th international conference on medical image computing and computer assisted intervention, MICCAI 2023

作者： Li, Jiawen Soon, Jaehyeon Zhang, Qilai Zhang, Qifan He, Yonghong Shenzhen International Graduate School Tsinghua University Shenzhen China Shenzhen China

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopia is a high-incidence disease that widely exists across various regions. If left unaddressed, it may escalate into high myopia. the leading cause of visual impairment is myopic maculopathy. Currently, certain deep-learning techniques have been employed for the analysis of images depicting myopic maculopathy in fundus photography. these methods are dedicated to assisting physicians in efficient disease diagnosis. In our work, a deep learning framework is introduced to classify images of five different severities of myopic maculopathy. First, we employ a diffusion model to generate a series of images for data augmentation to alleviate the pressure of uneven distribution of categories in training datasets, then we divide images into multiple patches and perform self-supervised learning to generate patch-level feature embeddings. Building upon the above foundation, an aggregator is proposed based on multiple instance learning to achieve image-level classification. We demonstrate the effectiveness of this method in four sufficient experiments with three key evaluation metrics of quadratic-weighted kappa, F1 score, and specificity. Our approach secured the tenth position in the Myopic Maculopathy Analysis Challenge 2023 (MICCAI MMAC 2023). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Supervised learning

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Brain Tumor Classification Using ML and DL Approaches 5

Brain Tumor Classification Using ML and DL Approaches

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5th IEEE international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Solanki, Shubhangi Singh, Uday Pratap Chouhan, Siddharth Singh LNCT University Department of Computer Science Engineering Bhopal462042 India School of Mathematics Shri Mata Vaishno Devi University Kakrayl Katra182320 India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798350338287

In medical image processing, categorizing brain tumors is one of the most critical and challenging challenges that must be solved. Because manual classification carried out with the assistance of humans often leads to inaccurate diagnoses and projections, we have turned to machine learning to help us. the proposed work includes a performance evaluation of autonomous brain tumor identification from MR imaging as well as a CT scan utilizing basic image processing approaches depending on multiple hard and soft computing techniques. Furthermore, six classical classifiers were used to identify brain tumors in the photos. then, to incorporate deep learning methods into our study, we used Convolutional Neural Networks and Deep Convolutional Neural Networks for brain tumor identification. the outcome of the conventional ML with the best accuracy method SVM was analyzed to the outcome of the conventional CNN. the performance of Deep CNN models is assessed, and it is discovered that VGG16 outperforms all other Deep learning models. When contrasted with these traditional classification methods, the SVM's findings were the most accurate. After that, a CNN is used, which, in contrast to the conventional classifiers, shows a discernible rise in overall performance that is much superior. It was decided to test several different layers of CNN with a variety of split ratios for the dataset. the outcomes of the experiments show that a five-layered CNN with an 80:20 split ratio is capable of achieving the greatest performance accuracy possible, which is 97.86%. © 2023 IEEE.

关键词： Convolutional neural networks

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ConTrans: Improving Transformer with Convolutional Attention for medical image Segmentation 25th

ConTrans: Improving Transformer with Convolutional Attention...

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

作者： Lin, Ailiang Xu, Jiayu Li, Jinxing Lu, Guangming Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9783031164439;9783031164422

Over the past few years, convolution neural networks (CNNs) and vision transformers (ViTs) have been two dominant architectures in medical image segmentation. Although CNNs can efficiently capture local representations, they experience difficulty establishing long-distance dependencies. Comparably, ViTs achieve impressive success owing to their powerful global contexts modeling capabilities, but they may not generalize well on insufficient datasets due to the lack of inductive biases inherent to CNNs. To inherit the merits of these two different design paradigms while avoiding their respective limitations, we propose a concurrent structure termed ConTrans, which can couple detailed localization information with global contexts to the maximum extent. ConTrans consists of two parallel encoders, i.e., a Swin Transformer encoder and a CNN encoder. Specifically, the CNN encoder is progressively stacked by the novel Depthwise Attention Block (DAB), with the aim to provide the precise local features we need. Furthermore, a well-designed Spatial-Reduction-Cross-Attention (SRCA) module is embedded in the decoder to form a comprehensive fusion of these two distinct feature representations and eliminate the semantic divergence between them. this allows to obtain accurate semantic information and ensure the up-sampling features with semantic consistency in a hierarchical manner. Extensive experiments across four typical tasks show that ConTrans significantly outperforms state-of-the-art methods on ten famous benchmarks.

关键词： medical image segmentation Transformer Convolution Neural Network Cross-attention

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A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT images 1

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

作者： Jiang, Hao Haimerl, Jason Gu, Xuejun Lu, Weiguo NeuralRad LLC 8517 Exelsior Dr MadisonWI53717 United States Stanford University StanfordCA94305 United States University of Texas Southwest Medical Center DallasTX75235 United States

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

Delineation of head and neck lesions are crucial for radiation treatment planning and follow-up studies. In this paper we developed an automated segmentation method for head and neck primary and nodal gross tumor volumes (GTVp and GTVn) segmentation in positron emission tomography/computed tomography (PET/CT) provided by the MICCAI 2022 Head and Neck Tumor Segmentation Challenge (HECKTOR 2022). Our segmentation algorithm takes nnU-Net as the backbone and uses dedicated pre- and post-processing to improve the auto-segmentation performance. the pipeline described achieved DSC results of 0.77212 (GTVp 0.77485 and GTVn 0.76938) in the testing dataset of HECTOR 2022. the developed auto-segmentation method is further extensively developed to a web-based platform to permit easy access and facilitate clinical workflow. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image segmentation

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Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT 25th

Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modal...

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

作者： Chen, Xiongchao Zhou, Bo Xie, Huidong Guo, Xueqi Zhang, Jiazhen Sinusas, Albert J. Onofrey, John A. Liu, Chi Yale Univ Dept Biomed Engn New Haven CT 06520 USA Yale Univ Dept Radiol & Biomed Imaging New Haven CT USA Yale Univ Dept Internal Med New Haven CT USA

ISBN: (纸本)9783031164460;9783031164453

Single-photon emission computed tomography (SPECT) is a widely applied imaging approach for diagnosis of coronary artery diseases. Attenuation maps (mu-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve diagnostic accuracy of cardiac SPECT. However, SPECT and CT are obtained sequentially in clinical practice, which potentially induces misregistration between the two scans. Convolutional neural networks (CNN) are powerful tools for medical image registration. Previous CNN-based methods for cross-modality registration either directly concatenated two input modalities as an early feature fusion or extracted image features using two separate CNN modules for a late fusion. these methods do not fully extract or fuse the cross-modality information. Besides, deep-learningbased rigid registration of cardiac SPECT and CT-derived mu-maps has not been investigated before. In this paper, we propose a Dual-Branch Squeeze-Fusion-Excitation (DuSFE) module for the registration of cardiac SPECT and CT-derived mu-maps. DuSFE fuses the knowledge from multiple modalities to recalibrate both channel-wise and spatial features for each modality. DuSFE can be embedded at multiple convolutional layers to enable feature fusion at different spatial dimensions. Our studies using clinical data demonstrated that a network embedded with DuSFE generated substantial lower registration errors and therefore more accurate AC SPECT images than previous methods. Our source code is available at https://***/XiongchaoChen/DuSFE_CrossRegistration.

关键词： Cardiac SPECT/CT Cross-modality image registration Attenuation correction

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Predicting Falls through Muscle Weakness from a Single Whole Body image: A Multimodal Contrastive Learning Framework 3rd

Predicting Falls Through Muscle Weakness from a Single Who...

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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： Zhang, Xia Saleem, Afsah Ilyas, Zaid Suter, David Nadeem, Uzair Prince, Richard L. Zhu, Kun Lewis, Joshua R. Sim, Marc Gilani, Syed Zulqarnain Centre for AI and ML School of Science Edith Cowan University Joondalup Australia Nutrition and Health Innovation Research Institute Edith Cowan University Joondalup Australia Computer Science and Software Engineering The University of Western Australia Perth Australia Medical School The University of Western Australia Perth Australia Department of Endocrinology and Diabetes Sir Charles Gairdner Hospital PerthWA Australia

ISBN: (纸本)9783031820069

Falls are often attributed to poor muscle function, with weak hand grip strength clinically recognized as a major risk factor. However, grip strength is rarely assessed clinically. Low radiation dual-energy X-ray absorptiometry (DXA) whole-body scans, that can be obtained during routine osteoporosis screening, offer a comprehensive overview of body composition, thereby providing valuable information for musculoskeletal health. Here, we propose a machine learning technique, exploiting image and clinical data to classify weak grip strength ( © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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