检索结果-内蒙古大学图书馆

the 19th international conference on medical image computing and computer-assisted intervention (MICCAI 2016)

medical image ANALYSIS 2017年 41卷 1-1页

作者： Ourselin, Sebastien Sabuncu, Mert R. Wells, William Joskowicz, Leo Unal, Gozde Maier, Andreas Univ Erlangen Nurnberg Dept Comp Sci Erlangen Germany

来源：评论

学校读者我要写书评

暂无评论

Preface

Lecture Notes in Computer Science (including subseries Lectu...

引用

Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2025年 15188 LNCS卷 v页

作者： Antony, Bhavna Lee, Cecilia S. Chen, Hao Fu, Huazhu Fang, Huihui Zheng, Yalin Federation University Australia Mount HelenVIC Australia University of Washington SeattleWA United States Hong Kong University of Science and Technology Kowloon Hong Kong Institute of High Performance Computing Singapore Singapore Pazhou Lab. Guangzhou China

来源：评论

学校读者我要写书评

暂无评论

the rise of AI language pathologists: exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification 23

The rise of AI language pathologists: exploring two-level pr...

引用

Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Linhao Qu Xiaoyuan Luo Kexue Fu Manning Wang Zhijian Song Digital Medical Research Center School of Basic Medical Science Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention

this paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4. Since a WSI is too large and needs to be divided into patches for processing, WSI classification is commonly approached as a Multiple Instance Learning (MIL) problem. In this context, each WSI is considered a bag, and the obtained patches are treated as instances. the objective of FSWC is to classify both bags and instances with only a limited number of labeled bags. Unlike conventional few-shot learning problems, FSWC poses additional challenges due to its weak bag labels within the MIL framework. Drawing inspiration from the recent achievements of vision-language models (V-L models) in downstream few-shot classification tasks, we propose a two-level prompt learning MIL framework tailored for pathology, incorporating language prior knowledge. Specifically, we leverage CLIP to extract instance features for each patch, and introduce a prompt-guided pooling strategy to aggregate these instance features into a bag feature. Subsequently, we employ a small number of labeled bags to facilitate few-shot prompt learning based on the bag features. Our approach incorporates the utilization of GPT-4 in a question-and-answer mode to obtain language prior knowledge at both the instance and bag levels, which are then integrated into the instance and bag level language prompts. Additionally, a learnable component of the language prompts is trained using the available few-shot labeled data. We conduct extensive experiments on three real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer, demonstrating the notable performance of the proposed method in bag and instance classification. Codes will be available at https://***/miccaiif/TOP.

关键词：

来源：评论

学校读者我要写书评

暂无评论

High Speed and Efficient Reconfigurable Histogram Equalisation Architecture for image Contrast Enhancement 8th

High Speed and Efficient Reconfigurable Histogram Equalisati...

引用

8th international conference on Information, Communication and computing Technology, ICICCT 2023

作者： Agalya, P. Hanumantharaju, M.C. Department of Electronics and Communication Engineering Sapthagiri College of Engineering Bangalore India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore India

ISBN: (纸本)9783031438370

Histogram equalisation is a point processing technique used to improve image quality in wide variety of applications including real-time video surveillance, medical imaging, industrial automation, intelligent self-navigation systems, and oceanography. this research paper proposes a unique, simple high-speed reconfigurable architecture for the implementation of histogram equalisation on field programmable gate arrays (FPGA). the proposed histogram equalisation architecture comprises of a pixel mapping unit and a histogram computing unit. the histogram computation unit makes use of counters and comparators to compute the cumulative histogram of images. the pixel mapping unit comprise of a delay control unit with a register array to map the output pixel intensity value for each input pixel grey level by adopting a straightforward and effective pixel transformation method. the proposed architecture has been developed using register transfer level (RTL) - compliant Verilog HDL code, simulated using Xilinx's integrated simulator (ISim), synthesized and implemented on a Kintex7 family of Field Programmable Gate Array (FPGA) device. Simulation and synthesis results demonstrate that the proposed architecture can be implemented with a processing time of 2.353 ns and a maximum frequency of operation of 425 MHz. Experimental results shows that the proposed method outperforms other existing methods. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

LogTrans: Providing Efficient Local-Global Fusion with Transformer and CNN Parallel Network for Biomedical image Segmentation

LogTrans: Providing Efficient Local-Global Fusion with Trans...

引用

IEEE international conference on High Performance computing and Communications (HPCC)

作者： Xingqing Nie Xiaogen Zhou Zhiqiang Li Luoyan Wang Xingtao Lin Tong Tong College of Physics and Information Engineering Fuzhou University Fuzhou China Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China

Accurate biomedical image segmentation is a prerequisite for excellent computer-aided diagnosis (CAD) systems. A series of researches have shown that convolutional neural networks (CNNs) have made impressive progress in segmentation tasks. Nevertheless, owing to the finite receptive field of CNN-based algorithms, such networks are focused too much on local area features rather than global context. While the Transformer architecture can encode global dependencies information through the self-attention mechanism, this mechanism typically ignores the local pixel-level structural information within each divided patch. therefore, a better solution is still needed for how to integrate CNN architecture with Transformer architecture efficiently. In this essay, we propose an originative parallel segmentation algorithm called LogTrans. First, in the encoder path, the local details and global contour dependencies on the entire image are captured by the CNN branch and the Transformer branch, respectively. then these two branches complement each other by a novel separate-combiner (SeCo) module, leading to better fused features. Moreover, we attempt to further enhance the segmentation properties by using a residual stackable dilated (ReS $D$ ) block, which applies residual shortcut connections to resolve dimension alterations in the target region and stacks dilated convolutions to capture more spatial information. the proposed LogTrans framework was evaluated on two biomedical datasets, including ISIC-2017 and UITNS-2022 datasets. Collectively, multiple results have indicated that our LogTrans performs superior with other state-of-the-art architectures in both visual comparison and quantitative appraisal.

关键词： image segmentation Visualization Fuses Transformers Decoding image restoration Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Approximate Nearest-Neighbour Fields via Massively-Parallel Propagation-assisted K-D Trees 8

Approximate Nearest-Neighbour Fields via Massively-Parallel ...

引用

8th IEEE international conference on Big Data (Big Data)

作者： Oancea, Cosmin Eugen Robroek, Ties Gieseke, Fabian Univ Copenhagen Dept Comp Sci DIKU Copenhagen Denmark Univ Munster Munster Germany

ISBN: (纸本)9781728162515

Nearest neighbour fields accurately and intuitively describe the transformation between two images and have been heavily used in computer vision. Generating such fields, however, is not an easy task due to the induced computational complexity, which quickly grows with the sizes of the images. Modern parallel devices such as graphics processing units depict a viable way of reducing the practical run time of such compute-intensive tasks. In this work, we propose a novel parallel implementation for one of the state-of-the-art methods for the computation of nearest neighbour fields, called propagation-assisted k-d trees. the resulting implementation yields valuable computational savings over a corresponding multi-core implementation. Additionally, it is tuned to consume only little additional memory and is, hence, capable of dealing with high-resolution image data, which is vital as image quality standards keep rising.

关键词： nearest neighbour fields propagation-assisted k-d trees high-performance computing computer vision

来源：评论

学校读者我要写书评

暂无评论

Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data 2nd

Progression Free Survival Prediction for Head and Neck Cance...

引用

2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on medical image computing and computer-assisted intervention, MICCAI 2021

作者： Naser, Mohamed A. Wahid, Kareem A. Mohamed, Abdallah S. R. Abdelaal, Moamen Abobakr He, Renjie Dede, Cem van Dijk, Lisanne V. Fuller, Clifton D. Department of Radiation Oncology The University of Texas MD Anderson Cancer HoustonTX77030 United States

ISBN: (纸本)9783030982522

Determining progression-free survival (PFS) for head and neck squamous cell carcinoma (HNSCC) patients is a challenging but pertinent task that could help stratify patients for improved overall outcomes. PET/CT images provide a rich source of anatomical and metabolic data for potential clinical biomarkers that would inform treatment decisions and could help improve PFS. In this study, we participate in the 2021 HECKTOR Challenge to predict PFS in a large dataset of HNSCC PET/CT images using deep learning approaches. We develop a series of deep learning models based on the DenseNet architecture using a negative log-likelihood loss function that utilizes PET/CT images and clinical data as separate input channels to predict PFS in days. Internal model validation based on 10-fold cross-validation using the training data (N = 224) yielded C-index values up to 0.622 (without) and 0.842 (with) censoring status considered in C-index computation, respectively. We then implemented model ensembling approaches based on the training data cross-validation folds to predict the PFS of the test set patients (N = 101). External validation on the test set for the best ensembling method yielded a C-index value of 0.694, placing 2nd in the competition. Our results are a promising example of how deep learning approaches can effectively utilize imaging and clinical data for medical outcome prediction in HNSCC, but further work in optimizing these processes is needed. © 2022, Springer Nature Switzerland AG.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification

arXiv

引用

arXiv 2024年

作者： Dorent, Reuben Khajavi, Roya Idris, Tagwa Ziegler, Erik Somarouthu, Bhanusupriya Jacene, Heather LaCasce, Ann Deissler, Jonathan Ehrhardt, Jan Engelson, Sofija Fischer, Stefan M. Gu, Yun Handels, Heinz Kasai, Satoshi Kondo, Satoshi Maier-Hein, Klaus Schnabel, Julia A. Wang, Guotai Wang, Litingyu Wald, Tassilo Yang, Guang-Zhong Zhang, Hanxiao Zhang, Minghui Pieper, Steve Harris, Gordon Kikinis, Ron Kapur, Tina Brigham and Women’s Hospital Harvard Medical School BostonMA United States Massachusetts General Hospital Harvard Medical School BostonMA United States Yunu Inc. CaryNC United States Isomics Inc CambridgeMA United States Dana-Farber Cancer Institute BostonMA United States Technical University Munich Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Munich Germany Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Institute of Medical Informatics University of Lübeck Lübeck Germany German Research Center for Artificial Intelligence Lübeck Germany Niigata University of Health and Welfare Niigata Japan Muroran Institute of Technology Hokkaido Japan Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China University of Electronic Science and Technology of China Chengdu China Heidelberg Germany University of Heidelberg Heidelberg Germany Shanghai AI laboratory Shanghai China

Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets. However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans. Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution. Despite the variety of weakly-supervised techniques proposed, most have been validated only on private datasets or small publicly available datasets. To address this limitation, the Mediastinal Lymph Node Quantification (LNQ) challenge was organized in conjunction with the 26th international conference on medical image computing and computer assisted intervention (MICCAI 2023). this challenge aimed to advance weakly-supervised segmentation methods by providing a new, partially annotated dataset and a robust evaluation framework. A total of 16 teams from 5 countries submitted predictions to the validation leaderboard, and 6 teams from 3 countries participated in the evaluation phase. the results highlighted both the potential and the current limitations of weakly-supervised approaches. On one hand, weakly-supervised approaches obtained relatively good performance with a median Dice score of 61.0%. On the other hand, top-ranked teams, with a median Dice score exceeding 70%, boosted their performance by leveraging smaller but fully annotated datasets to combine weak supervision and full supervision. this highlights both the promise of weakly-supervised methods and the ongoing need for high-quality, fully annotated data to achieve higher segmentation performance. . . © 2024, CC BY.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D medical image Segmentation based on Target-aware Domain Translation and Iterative Self-Training

arXiv

引用

arXiv 2022年

作者： Shin, Hyungseob Kim, Hyeongyu Kim, Sewon Jun, Yohan Eo, Taejoon Hwang, Dosik School of Electrical and Electronic Engineering Yonsei University Korea Republic of

Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance when in fully supervised condition. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation can alleviate this problem, which makes it possible to use annotated data in one imaging modality to train a network that can successfully perform segmentation on target imaging modality with no labels. In this work, we propose a self-training based unsupervised domain adaptation framework for 3D medical image segmentation named COSMOS and validate it with automatic segmentation of Vestibular Schwannoma (VS) and cochlea on high-resolution T2 Magnetic Resonance images (MRI). Our target-aware contrast conversion network translates source domain annotated T1 MRI to pseudo T2 MRI to enable segmentation training on target domain, while preserving important anatomical features of interest in the converted images. Iterative self-training is followed to incorporate unlabeled data to training and incrementally improve the quality of pseudo-labels, thereby leading to improved performance of segmentation. COSMOS won the 1st place in the Cross-Modality Domain Adaptation (crossMoDA) challenge held in conjunction with the 24th international conference on medical image computing and computer assisted intervention (MICCAI 2021). It achieves mean Dice score and Average Symmetric Surface Distance of 0.871±0.063 and 0.437±0.270 for VS, and 0.842±0.020 and 0.152±0.030 for cochlea. © 2022, CC BY-NC-SA.

关键词： Magnetic resonance

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Coarse-to-Fine Framework for the Segmentation of Clinical Target Volume 23rd

An Enhanced Coarse-to-Fine Framework for the Segmentation of...

引用

Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and thyroid Nodule Segmentation and Classification in Ultrasound images Challenge, TN-SCUI 2020 held in conjunction with 23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

作者： Chen, Huai Qian, Dahong Liu, Weiping Li, Hui Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Algorithm and Research Shanghai Aitrox Technology Co. Ltd. Shanghai China National Key Laboratory of Science and Technology on Nano/Micro Fabrication Key Laboratory for Thin Film and Micro Fabrication of the Ministry of Education Institute of Micro-Nano Science and Technology Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783030718268

In radiation therapy, obtaining accurate boundary of the clinical target volume (CTV) is the vital step to decrease the risk of treatment failures. However, it is a time-consuming and laborious task to obtain the delineation by hand. therefore, an automatic algorithm is urgently needed to realize accurate segmentation. In this paper, we propose an enhanced coarse-to-fine frameworkto automatically fuse the information of CT, T1 and T2 images to get the target region. this framework includes a coarse-segmentation stage to identify the region of interest (ROI) of targets and a fine-segmentation stage to iteratively refine the segmentation. In the coarse-segmentation stage, the F-loss is proposed to keep the high recall rate of the ROI. In the fine segmentation, the ROI of target will be first cropped according to the ROI obtained by coarse-segmentation and be fed into a 3D-Unet to get the initial results. then, the prediction and medium features will be set as additional information for the next one network to refine the results. When evaluated on the validation dataset of challenge of Anatomical Brain Barriers to Cancer Spread (ABCs), our method won the 3th place in the public leaderboard. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：