標(biāo)題: Titlebook: Cancer Prevention, Detection, and Intervention; Third MICCAI Worksho Sharib Ali,Fons van der Sommen,Iris Kolenbrander Conference proceeding [打印本頁] 作者: 夾子 時間: 2025-3-21 18:27
書目名稱Cancer Prevention, Detection, and Intervention影響因子(影響力)
書目名稱Cancer Prevention, Detection, and Intervention影響因子(影響力)學(xué)科排名
書目名稱Cancer Prevention, Detection, and Intervention網(wǎng)絡(luò)公開度
書目名稱Cancer Prevention, Detection, and Intervention網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Cancer Prevention, Detection, and Intervention被引頻次
書目名稱Cancer Prevention, Detection, and Intervention被引頻次學(xué)科排名
書目名稱Cancer Prevention, Detection, and Intervention年度引用
書目名稱Cancer Prevention, Detection, and Intervention年度引用學(xué)科排名
書目名稱Cancer Prevention, Detection, and Intervention讀者反饋
書目名稱Cancer Prevention, Detection, and Intervention讀者反饋學(xué)科排名
作者: Volatile-Oils 時間: 2025-3-22 00:12 作者: 大炮 時間: 2025-3-22 01:45 作者: Jacket 時間: 2025-3-22 06:39 作者: 射手座 時間: 2025-3-22 11:44 作者: 貞潔 時間: 2025-3-22 15:29
0302-9743 sections as follows: Classification and characterization; detection and segmentation; cancer/early cancer detection, treatment and survival prognosis..978-3-031-73375-8978-3-031-73376-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 貞潔 時間: 2025-3-22 17:53
Conference proceedings 20256, 2024...The 22 full papers presented in this book were carefully reviewed and selected from 25 submissions. They were organized in topical sections as follows: Classification and characterization; detection and segmentation; cancer/early cancer detection, treatment and survival prognosis..作者: 誓言 時間: 2025-3-22 22:08 作者: Altitude 時間: 2025-3-23 03:02 作者: Asseverate 時間: 2025-3-23 06:16 作者: osculate 時間: 2025-3-23 10:41
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242018.jpg作者: Culmination 時間: 2025-3-23 17:05 作者: OREX 時間: 2025-3-23 18:56 作者: 好色 時間: 2025-3-23 22:18
Cancer Prevention, Detection, and Intervention978-3-031-73376-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 范圍廣 時間: 2025-3-24 03:23 作者: 歌曲 時間: 2025-3-24 08:10 作者: Entrancing 時間: 2025-3-24 13:09
Pedro Rodrigues,Filipe Freitas,José Sim?oosis system for early detection and enhanced treatment. Traditional approaches rely on the expertise of gastroenterologists to identify diseases. However, it is a subjective process, and the interpretation can vary even between expert clinicians. Considering recent progress in classifying gastrointe作者: Oration 時間: 2025-3-24 17:55 作者: patriarch 時間: 2025-3-24 21:41
Armando Ruggeri,Massimo Villariarning has emerged as a potential solution to this problem. In this work, we leverage the strengths of meta-learning, the primary framework for few-shot learning, along with diffusion-based generative models to enhance few-shot learning capabilities. We propose a novel method that jointly trains a d作者: Ingrained 時間: 2025-3-25 01:49 作者: CHOIR 時間: 2025-3-25 04:38
Jian Wang,Panpan Gao,Yutao Ma,Keqing Heetic resonance images (MRI) of the prostate. Most MRI-based systems are designed to detect clinically significant PC lesions, with the main objective of preventing over-diagnosis. Typically, these systems involve an automatic prostate segmentation component and a clinically significant PC lesion det作者: adumbrate 時間: 2025-3-25 09:24 作者: Carcinogenesis 時間: 2025-3-25 14:36 作者: 字形刻痕 時間: 2025-3-25 18:21 作者: aggravate 時間: 2025-3-25 22:01 作者: 想象 時間: 2025-3-26 01:28 作者: BOOM 時間: 2025-3-26 05:48
Abdelghafor Elgamri,Banmali S. Rawatx should employ flexible or rigid endoscopes to identify early-stage lesions, possibly enhanced with advanced imaging techniques such as Narrow Band Imaging (NBI) to empower tissue visualization. Factors that make the detection, diagnosis, and treatment of LC challenging include the huge amount of u作者: Adenoma 時間: 2025-3-26 12:23
Pratit Nayak,Ekta Nashine,Sanjeet Kumaris compounded by the high intra-class variability, where different slices of a 3D object can appear drastically different in 2D images, and low inter-class variance, where pathological features are often small and subtle compared to the rest of the image. These factors make it difficult to train mod作者: 紅腫 時間: 2025-3-26 16:02 作者: 粗魯性質(zhì) 時間: 2025-3-26 18:13 作者: transplantation 時間: 2025-3-26 23:34 作者: mendacity 時間: 2025-3-27 04:55
FoTNet Enables Preoperative Differentiation of?Malignant Brain Tumors with?Deep Learnings. Accurate preoperative differentiation is essential for appropriate treatment planning and prognosis, however, it’s challenging to differentiate these tumors using MRI due to their similar anatomical structures and imaging characteristics. In this paper, we first construct a new multi-center brain作者: 美色花錢 時間: 2025-3-27 07:54
Classification of?Endoscopy and?Video Capsule Images Using CNN-Transformer Modelosis system for early detection and enhanced treatment. Traditional approaches rely on the expertise of gastroenterologists to identify diseases. However, it is a subjective process, and the interpretation can vary even between expert clinicians. Considering recent progress in classifying gastrointe作者: APEX 時間: 2025-3-27 10:58
Multimodal Deep Learning-Based Prediction of?Immune Checkpoint Inhibitor Efficacy in?Brain Metastaseever, a predictive biomarker for ICI efficacy is needed to inform precision-based use of ICI given its high toxicity rate. Here, we present several multimodal deep learning (DL) approaches that integrate pre-treatment magnetic resonance imaging (MRI) and clinical metadata to predict ICI efficacy for作者: indigenous 時間: 2025-3-27 17:33 作者: 冥界三河 時間: 2025-3-27 19:35
Performance Evaluation of?Deep Learning and?Transformer Models Using Multimodal Data for?Breast Cancmance in BC classification compared to human expert readers. However, the predominant use of unimodal (digital mammography) features may limit the current performance of diagnostic models. To address this, we collected a novel multimodal dataset comprising both imaging and textual data. This study p作者: 嬰兒 時間: 2025-3-27 22:20
On Undesired Emergent Behaviors in?Compound Prostate Cancer Detection Systemsetic resonance images (MRI) of the prostate. Most MRI-based systems are designed to detect clinically significant PC lesions, with the main objective of preventing over-diagnosis. Typically, these systems involve an automatic prostate segmentation component and a clinically significant PC lesion det作者: 放逐某人 時間: 2025-3-28 02:32 作者: 種屬關(guān)系 時間: 2025-3-28 06:52
Automated Hepatocellular Carcinoma Analysis in?Multi-phase CT with?Deep Learningns with intravenous contrast in multiple phases, taken at different intervals post-injection. Organ movement during these intervals, caused by factors like breathing, heartbeat, or patient motion, can affect the accuracy of HCC detection. Aligning two or more scans precisely, especially ensuring the作者: 送秋波 時間: 2025-3-28 10:28
Refining Deep Learning Segmentation Maps with?a?Local Thresholding Approach: Application to?Liver SuCT imaging can be challenging and is often subject to disagreements between radiologists. The nodularity of the liver surface is a well-known feature of fibrosis, which can be quantified in clinical practice with specialized software applications that rely on semi-automatic delineation of the liver 作者: Allodynia 時間: 2025-3-28 17:39 作者: Charlatan 時間: 2025-3-28 19:57
Generalized Polyp Detection from?Colonoscopy Frames Using Proposed EDF-YOLO8 Networkdisposing factor. Early polyp identification and removal-the precursors to colorectal cancer-is essential to its prevention. Colonoscopy is considered the gold standard for colorectal cancer screening because it allows for the immediate removal of polyps, preventing them from developing into cancer.作者: 歌唱隊(duì) 時間: 2025-3-28 23:44 作者: Increment 時間: 2025-3-29 05:56 作者: transplantation 時間: 2025-3-29 09:51 作者: 龍蝦 時間: 2025-3-29 12:43
AI Age Discrepancy: A Novel Parameter for?Frailty Assessment in?Kidney Tumor Patientss AI Age Discrepancy, a novel metric derived from machine learning analysis of preoperative abdominal CT scans, as a potential indicator of frailty and postoperative risk in kidney cancer patients. This retrospective study of 599 patients from the 2023 Kidney Tumor Segmentation (KiTS) challenge data作者: 形狀 時間: 2025-3-29 15:59 作者: irreparable 時間: 2025-3-29 22:04 作者: 打折 時間: 2025-3-30 01:13
Armando Ruggeri,Massimo Villarited sample and the original class prototype, i.e., derived solely from the original support samples. Evaluations on two tumor characterization tasks (prostate cancer aggressiveness and breast cancer malignity assessment) demonstrate our approach’s effectiveness in improving prototype representation 作者: 不要嚴(yán)酷 時間: 2025-3-30 07:24 作者: inflate 時間: 2025-3-30 09:55
Jian Wang,Panpan Gao,Yutao Ma,Keqing Hewe simulate a realistic deployment scenario and evaluate the effect of two non-ideal and previously validated prostate segmentation modules on the PC detection ability of the compound system. Following, we compare them with an idealized setting, where prostate segmentations are assumed to have no fa作者: 明確 時間: 2025-3-30 13:38 作者: Celiac-Plexus 時間: 2025-3-30 19:13 作者: 錯 時間: 2025-3-30 22:33 作者: DECRY 時間: 2025-3-31 02:58
Klaus-Peter F?hnrich,Thomas MeirenI sequences. The model achieved an Area Under the Curve (AUC) of 0.79 across all MRI sequences. In the optimal setup, it classified . of predictions as certain and . as uncertain, with an AUC of 0.9 for certain predictions. These results clearly demonstrate the model’s efficacy in accurately quantif作者: 平息 時間: 2025-3-31 08:23
