標題: Titlebook: Data Engineering in Medical Imaging; First MICCAI Worksho Binod Bhattarai,Sharib Ali,Danail Stoyanov Conference proceedings 2023 The Editor [打印本頁] 作者: 審美家 時間: 2025-3-21 18:01
書目名稱Data Engineering in Medical Imaging影響因子(影響力)
書目名稱Data Engineering in Medical Imaging影響因子(影響力)學科排名
書目名稱Data Engineering in Medical Imaging網絡公開度
書目名稱Data Engineering in Medical Imaging網絡公開度學科排名
書目名稱Data Engineering in Medical Imaging被引頻次
書目名稱Data Engineering in Medical Imaging被引頻次學科排名
書目名稱Data Engineering in Medical Imaging年度引用
書目名稱Data Engineering in Medical Imaging年度引用學科排名
書目名稱Data Engineering in Medical Imaging讀者反饋
書目名稱Data Engineering in Medical Imaging讀者反饋學科排名
作者: 無價值 時間: 2025-3-21 21:32 作者: ureter 時間: 2025-3-22 03:30
Conference proceedings 2023d process by at least three members of the scientific review committee, comprising 16 experts in the field of medical imaging. The accepted manuscripts cover various medical image analysis methods and applications..作者: DECRY 時間: 2025-3-22 05:21
0302-9743 ouble-blind process by at least three members of the scientific review committee, comprising 16 experts in the field of medical imaging. The accepted manuscripts cover various medical image analysis methods and applications..978-3-031-44991-8978-3-031-44992-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: flaunt 時間: 2025-3-22 10:15
0302-9743 d Intervention, MICCAI 2023, which was held in Vancouver, Canada in October 2023..The DEMI 2023 proceedings contain 11 high-quality papers of 9 to 15 pages pre-selected through a rigorous peer review process (with an average of three reviews per paper). All submissions were peer-reviewed through a d作者: Albinism 時間: 2025-3-22 16:58
A. Berlin,A. E. Bennett,W. J. Hunter various data augmentation techniques was also conducted. Finally, the manual tumor segmentation and annotation step performed by the pathologists was assessed. Our proposed training scheme outperformed SOTA by 3.73%. Source code is available ..作者: Albinism 時間: 2025-3-22 17:22 作者: cyanosis 時間: 2025-3-23 01:04
,Whole Slide Multiple Instance Learning for?Predicting Axillary Lymph Node Metastasis, various data augmentation techniques was also conducted. Finally, the manual tumor segmentation and annotation step performed by the pathologists was assessed. Our proposed training scheme outperformed SOTA by 3.73%. Source code is available ..作者: 多余 時間: 2025-3-23 02:07
,Vision Transformer-Based Self-supervised Learning for?Ulcerative Colitis Grading in?Colonoscopy,s in UC. To take full advantage of local and global features, we propose to use Swin Transformers in the MoCo-v3 SSL setting. In addition, we provide a comprehensive benchmarking of other existing SSL methods. Our approach with Swin Transformer with MoCo-v3 provides performance boosts in different data size settings.作者: 動機 時間: 2025-3-23 06:08 作者: corn732 時間: 2025-3-23 10:05 作者: EVEN 時間: 2025-3-23 15:30
,A Client-Server Deep Federated Learning for?Cross-Domain Surgical Image Segmentation,common to both the source and target domains. The clients consist of the respective domain-specific parameters and make requests to the server while learning their parameters and inferencing. We evaluate our framework in two benchmark datasets, demonstrating applicability in computer-assisted interv作者: deadlock 時間: 2025-3-23 19:53 作者: 打擊 時間: 2025-3-24 02:09 作者: 假設 時間: 2025-3-24 02:58 作者: Pulmonary-Veins 時間: 2025-3-24 10:31 作者: tackle 時間: 2025-3-24 12:32
,Investigating Transformer Encoding Techniques to?Improve Data-Driven Volume-to-Surface Liver Registation. However, view occlusion, lack of meaningful feature landmarks, and liver deformation between the pre- and intra-operative settings all contribute to the difficulty of this registration task. In this work, we leverage some of the state-of-the-art deep learning frameworks to implement and test 作者: 違法事實 時間: 2025-3-24 16:18
,Task-Guided Domain Gap Reduction for?Monocular Depth Prediction in?Endoscopy,rom real data and overfit to synthetic anatomies and properties. This work proposes a novel approach to leverage labeled synthetic and unlabeled real data. While previous domain adaptation methods indiscriminately enforce the distributions of both input data modalities to coincide, we focus on the e作者: Charlatan 時間: 2025-3-24 19:07 作者: 貧窮地活 時間: 2025-3-24 23:38 作者: 他日關稅重重 時間: 2025-3-25 03:55 作者: 畸形 時間: 2025-3-25 09:42
A European Overview of the Health Care Scenees for improved data curation. Additionally, the tool facilitates quality control and review, enabling researchers to validate image and segmentation quality in large datasets. It also plays a critical role in uncovering potential biases in datasets by aggregating and visualizing metadata, which is 作者: extinguish 時間: 2025-3-25 15:25
Phototoxic Changes in the Retinadatasets). Mean DICE score across all models for liver segmentation increased by 15% (p=0.02) after pre-training on synthetic data. For polyp detection, Precision increased by 11% (p=0.002), Recall by 9% (p=0.01), mAP@.5 by 10% (p=0.01) and mAP@[.5:95] by 8% (p-0.003)..All synthetic data, as well as作者: GUMP 時間: 2025-3-25 16:08 作者: subacute 時間: 2025-3-25 20:45
Light-Induced Changes in Ocular Tissuesation. However, view occlusion, lack of meaningful feature landmarks, and liver deformation between the pre- and intra-operative settings all contribute to the difficulty of this registration task. In this work, we leverage some of the state-of-the-art deep learning frameworks to implement and test 作者: 周興旺 時間: 2025-3-26 02:29 作者: BARB 時間: 2025-3-26 06:35
,Weakly Supervised Medical Image Segmentation Through Dense Combinations of?Dense Pseudo-Labels,. Instead, obtaining less precise scribble–like annotations is more feasible for clinicians. In this context, training semantic segmentation networks with limited-signal supervision remains a technical challenge. We present an innovative scribble-supervised approach to image segmentation via densely作者: Cacophonous 時間: 2025-3-26 10:07 作者: CUMB 時間: 2025-3-26 13:17 作者: Flounder 時間: 2025-3-26 18:01 作者: pester 時間: 2025-3-27 00:43
,Efficient Large Scale Medical Image Dataset Preparation for?Machine Learning Applications,e effectiveness of these algorithms is contingent upon the availability and organization of high-quality medical imaging datasets. Traditional Digital Imaging and Communications in Medicine (DICOM) data management systems are inadequate for handling the scale and complexity of data required to be fa作者: phlegm 時間: 2025-3-27 04:21 作者: BAN 時間: 2025-3-27 08:16 作者: Collision 時間: 2025-3-27 11:18 作者: Abrade 時間: 2025-3-27 16:10 作者: diathermy 時間: 2025-3-27 20:43
,Vision Transformer-Based Self-supervised Learning for?Ulcerative Colitis Grading in?Colonoscopy,ed to stratify patients at higher risk of developing colorectal cancer, the phenotypic endoscopic features involved in the scoring are highly inconsistent. Thus, devising automated methods is required. However, bias in the labels can also trigger such inconsistency and inaccuracy, which makes the us作者: Presbycusis 時間: 2025-3-27 22:31
,Task-Guided Domain Gap Reduction for?Monocular Depth Prediction in?Endoscopy, improve the quality and availability of colonoscopies by automatizing sub-tasks. One such task is predicting depth from monocular video frames, which can assist endoscopic navigation. As ground truth depth from standard in-vivo colonoscopy remains unobtainable due to hardware constraints, two appro作者: ROOF 時間: 2025-3-28 02:45
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/262794.jpg作者: 招募 時間: 2025-3-28 07:42
https://doi.org/10.1007/978-3-031-44992-5Informatics; Medical Imaging; Data and Label Augmentation; Active Learning; Active Synthesis; Federated L作者: OMIT 時間: 2025-3-28 11:02
978-3-031-44991-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 奴才 時間: 2025-3-28 14:43
Data Engineering in Medical Imaging978-3-031-44992-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 雜役 時間: 2025-3-28 19:38 作者: 阻止 時間: 2025-3-28 23:59
A. Berlin,A. E. Bennett,W. J. Hunteraluation and treatment guidance. This paper presents a deep learning (DL) classification pipeline for quantifying clinical information from digital core-needle biopsy (CNB) images, with one step less than existing methods. A publicly available dataset of 1058 patients was used to evaluate the perfor作者: Militia 時間: 2025-3-29 03:37 作者: duplicate 時間: 2025-3-29 11:11
https://doi.org/10.1007/978-94-009-3197-8rce to develop a better performing deep learning model from scratch. Transfer learning with networks pre-trained on ImageNet is commonly applied to address this problem. FDSL techniques have been recently investigated as an alternative solution to ImageNet based approaches. In the FDSL setting, netw作者: Culpable 時間: 2025-3-29 14:41
A European Overview of the Health Care Scenee effectiveness of these algorithms is contingent upon the availability and organization of high-quality medical imaging datasets. Traditional Digital Imaging and Communications in Medicine (DICOM) data management systems are inadequate for handling the scale and complexity of data required to be fa作者: insular 時間: 2025-3-29 17:22
Light-Induced Changes in the Skin of the Lidscope navigates the anatomy. However, this task is made challenging by the lack of discriminative localisation landmarks throughout the colon. While standard navigation techniques rely on sparse point landmarks or dense pixel registration, we propose edges as a more natural visual landmark to charac作者: Arthr- 時間: 2025-3-29 19:44
Phototoxic Changes in the Retinaing data can be difficult to obtain, or of limited size. Procedural generation of data allows for large datasets to be rapidly generated and automatically labelled, while also randomising relevant parameters within the simulation to provide a wide variation in models and textures used in the scene..作者: 祖?zhèn)髫敭a 時間: 2025-3-30 02:45
Clinical Light Damage to the Eyeactors. For natural image classification training with noisy labeled data, model initialization with contrastive self-supervised pretrained weights has shown to reduce feature corruption and improve classification performance. However, no works have explored: i) how other self-supervised approaches,
