標(biāo)題: Titlebook: Cancer Prevention Through Early Detection; Second International Sharib Ali,Fons van der Sommen,Iris Kolenbrander Conference proceedings 202 [打印本頁(yè)] 作者: 浮淺 時(shí)間: 2025-3-21 19:40
書目名稱Cancer Prevention Through Early Detection影響因子(影響力)
書目名稱Cancer Prevention Through Early Detection影響因子(影響力)學(xué)科排名
書目名稱Cancer Prevention Through Early Detection網(wǎng)絡(luò)公開(kāi)度
書目名稱Cancer Prevention Through Early Detection網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Cancer Prevention Through Early Detection被引頻次
書目名稱Cancer Prevention Through Early Detection被引頻次學(xué)科排名
書目名稱Cancer Prevention Through Early Detection年度引用
書目名稱Cancer Prevention Through Early Detection年度引用學(xué)科排名
書目名稱Cancer Prevention Through Early Detection讀者反饋
書目名稱Cancer Prevention Through Early Detection讀者反饋學(xué)科排名
作者: Concerto 時(shí)間: 2025-3-21 23:55 作者: Oration 時(shí)間: 2025-3-22 01:51 作者: Bereavement 時(shí)間: 2025-3-22 04:38
Image Captioning for?Automated Grading and?Understanding of?Ulcerative Colitis severity of UC is done by using a widely accepted scoring system known as the “Mayo Endoscopic Scoring” (MES). The MES score is largely based on the recognition of phenotypic features of the mucosal wall, and thus the subjectivity in clinical scoring is unavoidable. An automated grading and charact作者: GEST 時(shí)間: 2025-3-22 09:21 作者: circumvent 時(shí)間: 2025-3-22 14:16
Assessing the?Performance of?Deep Learning-Based Models for?Prostate Cancer Segmentation Using Uncer The aim is to improve the workflow of prostate cancer detection and diagnosis. Seven different U-Net-based architectures, augmented with Monte-Carlo dropout, are evaluated for automatic segmentation of the central zone, peripheral zone, transition zone, and tumor, with uncertainty estimation. The t作者: circumvent 時(shí)間: 2025-3-22 20:45 作者: JAMB 時(shí)間: 2025-3-22 22:56
Colonoscopy Coverage Revisited: Identifying Scanning Gaps in?Real-Time Recent studies show that around 25% of the existing polyps are routinely missed. While some of these do appear in the endoscopist’s field of view, others are missed due to a partial coverage of the colon. The task of detecting and marking unseen regions of the colon has been addressed in recent wor作者: PUT 時(shí)間: 2025-3-23 02:01
ColNav: Real-Time Colon Navigation for?Colonoscopysions and provides the ability to remove them during the procedure itself. Nevertheless, failure by the endoscopist to identify such lesions increases the likelihood of lesion progression to subsequent colorectal cancer. Ultimately, colonoscopy remains operator-dependent, and the wide range of quali作者: motivate 時(shí)間: 2025-3-23 07:31 作者: 缺乏 時(shí)間: 2025-3-23 13:17 作者: Exploit 時(shí)間: 2025-3-23 16:10 作者: 外形 時(shí)間: 2025-3-23 21:32
Modeling Barrett’s Esophagus Progression Using Geometric Variational Autoencodersonstruction loss, and generative capacity. Additionally, we present a novel autoencoder architecture that can generate qualitative images without the need for a variational framework while retaining the benefits of an autoencoder, such as improved stability and reconstruction quality.作者: Ingrained 時(shí)間: 2025-3-23 22:37 作者: 松緊帶 時(shí)間: 2025-3-24 05:52
0302-9743 ubmit their work in the field of medical image analysis around the central theme of cancer and early cancer detection, progression, inflammation understanding, multimodality data, and computer-aided navigation..978-3-031-45349-6978-3-031-45350-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 通知 時(shí)間: 2025-3-24 07:45 作者: affect 時(shí)間: 2025-3-24 13:51 作者: 精確 時(shí)間: 2025-3-24 16:12
Christopher J. Lucas,John W. Murry Jr.onstruction loss, and generative capacity. Additionally, we present a novel autoencoder architecture that can generate qualitative images without the need for a variational framework while retaining the benefits of an autoencoder, such as improved stability and reconstruction quality.作者: 無(wú)畏 時(shí)間: 2025-3-24 19:33 作者: 流浪 時(shí)間: 2025-3-25 03:03 作者: ferment 時(shí)間: 2025-3-25 05:42 作者: PAC 時(shí)間: 2025-3-25 09:31 作者: 脊椎動(dòng)物 時(shí)間: 2025-3-25 14:38 作者: Forage飼料 時(shí)間: 2025-3-25 19:12
Christopher J. Lucas,John W. Murry Jr. The aim is to improve the workflow of prostate cancer detection and diagnosis. Seven different U-Net-based architectures, augmented with Monte-Carlo dropout, are evaluated for automatic segmentation of the central zone, peripheral zone, transition zone, and tumor, with uncertainty estimation. The t作者: Mediocre 時(shí)間: 2025-3-25 22:28 作者: subordinate 時(shí)間: 2025-3-26 04:04
Christopher J. Lucas,John W. Murry Jr. Recent studies show that around 25% of the existing polyps are routinely missed. While some of these do appear in the endoscopist’s field of view, others are missed due to a partial coverage of the colon. The task of detecting and marking unseen regions of the colon has been addressed in recent wor作者: 個(gè)人長(zhǎng)篇演說(shuō) 時(shí)間: 2025-3-26 05:56 作者: 出價(jià) 時(shí)間: 2025-3-26 09:58 作者: STANT 時(shí)間: 2025-3-26 14:02 作者: ALLAY 時(shí)間: 2025-3-26 20:37 作者: saphenous-vein 時(shí)間: 2025-3-26 22:43
https://doi.org/10.1007/978-3-031-45350-2medical image analysis; machine learning; deep learning; lesion classification; lesion detection; lesion 作者: 細(xì)微差別 時(shí)間: 2025-3-27 04:48 作者: 控制 時(shí)間: 2025-3-27 08:24
A Deep Attention-Multiple Instance Learning Framework to?Predict Survival of?Soft-Tissue Sarcoma frotted from the Deep Attention-MIL model are used to divide the patients into low/high-risk groups and predict survival time. The framework was trained and validated on a local dataset including 220 patients, then it was used to predict the survival for 48 patients in an external validation dataset. T作者: 北京人起源 時(shí)間: 2025-3-27 10:37 作者: 名義上 時(shí)間: 2025-3-27 15:51
Fully Automated CAD System for?Lung Cancer Detection and?Classification Using 3D Residual U-Net withxtensive experimental results illustrate the effectiveness of our 3D residual U-Net model. These results demonstrate the exceptional detection performance achieved by our proposed model with a sensitivity of 97.65% and an average classification accuracy of 96.37%. Performance analysis demonstrates t作者: 吹氣 時(shí)間: 2025-3-27 18:39 作者: 率直 時(shí)間: 2025-3-28 01:03
Multispectral 3D Masked Autoencoders for?Anomaly Detection in?Non-Contrast Enhanced Breast MRI-cancerous images are presented to the model, with the purpose of localizing anomalous tumor regions during test time. We use a public dataset for model development. Performance of the architecture is evaluated in reference to subtraction images created from DCE-MRI. Code has been made publicly avai作者: 顛簸地移動(dòng) 時(shí)間: 2025-3-28 02:36 作者: 閑蕩 時(shí)間: 2025-3-28 09:21 作者: 命令變成大炮 時(shí)間: 2025-3-28 11:01
ColNav: Real-Time Colon Navigation for?Colonoscopyure, providing actionable and comprehensible guidance to un-surveyed areas in real-time, while seamlessly integrating into the physician’s workflow. Through coverage experimental evaluation, we demonstrated that our system resulted in a higher polyp recall (PR) and high inter-rater reliability with 作者: Optic-Disk 時(shí)間: 2025-3-28 15:40
Nives Mazur Kumri?,Mirela ?upantted from the Deep Attention-MIL model are used to divide the patients into low/high-risk groups and predict survival time. The framework was trained and validated on a local dataset including 220 patients, then it was used to predict the survival for 48 patients in an external validation dataset. T作者: Trochlea 時(shí)間: 2025-3-28 20:34 作者: 整潔 時(shí)間: 2025-3-29 00:52 作者: Endearing 時(shí)間: 2025-3-29 06:11
https://doi.org/10.1007/978-3-642-22839-1ugh a recurrent neural network to predict such scene descriptions. In this work, we explore various recurrent neural network architectures together with other backbone architectures for visual feature representations. Our experiments on held-out test samples demonstrate high similarity between the r作者: 吃掉 時(shí)間: 2025-3-29 08:49
,Deep sea, deep snow … deep space,-cancerous images are presented to the model, with the purpose of localizing anomalous tumor regions during test time. We use a public dataset for model development. Performance of the architecture is evaluated in reference to subtraction images created from DCE-MRI. Code has been made publicly avai
