標題: Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay [打印本頁] 作者: Iodine 時間: 2025-3-21 17:44
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023影響因子(影響力)
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023影響因子(影響力)學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023網(wǎng)絡公開度
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023網(wǎng)絡公開度學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023被引頻次
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023被引頻次學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023年度引用
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023年度引用學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023讀者反饋
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023讀者反饋學科排名
作者: 蕨類 時間: 2025-3-22 00:13 作者: MIRE 時間: 2025-3-22 01:00 作者: accomplishment 時間: 2025-3-22 07:07
Ruipeng Zhang,Ziqing Fan,Qinwei Xu,Jiangchao Yao,Ya Zhang,Yanfeng Wangsch“ noch eine sinnvolle analytische Kategorie ist und ob es so etwas wie eine ostdeutsche kollektive Identit?t gibt. Dieser Beitrag skizziert einleitend vier prototypische Standpunkte in der Diskussion um Ostdeutschland in ihren Grundzügen und als Bestandteil einer ?ffentlichen Auseinandersetzung u作者: oxidize 時間: 2025-3-22 10:18
Shixuan Chen,Boxuan Cao,Yinda Du,Yaoduo Zhang,Ji He,Zhaoying Bian,Dong Zeng,Jianhua Mane Budget!.Du baust Dein eigenes Gesch?ft auf, brauchst dafür Online-Marketing, hast aber nicht viel Geld zur Verfügung? Du führst ein kleines Gesch?ft und willst jetzt endlich mit Online-Marketing starten, wei?t aber noch nicht, wie das sinnvoll geht? Dann lies – und arbeite – mit diesem Buch!.?Onl作者: ANA 時間: 2025-3-22 14:52 作者: refine 時間: 2025-3-22 17:57 作者: 細頸瓶 時間: 2025-3-22 23:33 作者: PAD416 時間: 2025-3-23 01:44 作者: Baffle 時間: 2025-3-23 07:54
Zhipeng Deng,Luyang Luo,Hao Chen Bereich Learning & Development unter dem Einfluss der Digit.Das Buch zeigt anhand aktueller Forschungsthemen und Praxisbeispiele, welche Ver?nderungen die digitale Transformation in den Lernprozessen von und in Organisationen ausgel?st hat.?.Anpassungen von Organisationen auf Ver?nderungen sind die作者: 激怒某人 時間: 2025-3-23 10:16
Amandeep Kumar,Ankan Kumar Bhunia,Sanath Narayan,Hisham Cholakkal,Rao Muhammad Anwer,Jorma Laaksonenrde im Herbst 1944 gegründet als ?Reichsinstitut für Mathematik“, dessen Aufgaben sehr umfassend waren, aber v?llig im zeitgen?ssischen institutionellen Rahmen blieben. In den 1950er und 1960er Jahren entwickelte das MFO sich zu einem Tagungszentrum, das zunehmend auch international ausstrahlte, d.?作者: Liberate 時間: 2025-3-23 15:29 作者: cacophony 時間: 2025-3-23 21:05
Mayank Gupta,Soumen Basu,Chetan Arorarde im Herbst 1944 gegründet als ?Reichsinstitut für Mathematik“, dessen Aufgaben sehr umfassend waren, aber v?llig im zeitgen?ssischen institutionellen Rahmen blieben. In den 1950er und 1960er Jahren entwickelte das MFO sich zu einem Tagungszentrum, das zunehmend auch international ausstrahlte, d.?作者: 平躺 時間: 2025-3-24 00:40
Xuan Chen,Weiheng Fu,Tian Li,Xiaoshuang Shi,Hengtao Shen,Xiaofeng Zhu sich besser in Forschungseinrichtungen au?erhalb der HochscDas Open-Access-Buch untersucht, wie die organisationale Gestaltung von Forschungseinrichtungen ihre inhaltliche und methodische Ausrichtung beeinflusst und wie sich Disziplinen und Forschungsfelder im Laufe der Zeit gewandelt und an neue i作者: 場所 時間: 2025-3-24 06:13 作者: 艱苦地移動 時間: 2025-3-24 08:50
Charles Jones,Mélanie Roschewitz,Ben Glocker Sp?testens seit der industriellen Revolution geh?ren Versuchsanstalten zur institutionellen ?Grundausstattung“ entwickelter Forschungs- und Innovationssysteme, weil Universit?ten und Hochschulen bei der Bearbeitung von Fragestellungen aus der Praxis (z.?B. von Unternehmen) an organisatorische und d作者: 不愿 時間: 2025-3-24 12:07 作者: dyspareunia 時間: 2025-3-24 18:52
GRACE: A Generalized and?Personalized Federated Learning Method for?Medical Imagingscenarios still greatly limits its practice, which requires to consider both generalization and personalization, namely generalized and personalized federated learning (GPFL). Previous studies almost focus on the partial objective of GPFL: personalized federated learning mainly cares about its local作者: exquisite 時間: 2025-3-24 21:38
Chest X-ray Image Classification: A Causal Perspectiveancements in deep learning-based methods capable of effectively classifying CXR. However, assessing whether these algorithms truly capture the cause-and-effect relationship between diseases and their underlying causes, or merely learn to map labels to images, remains a challenge. In this paper, we p作者: farewell 時間: 2025-3-25 00:30 作者: inclusive 時間: 2025-3-25 03:23
Federated Condition Generalization on?Low-dose CT Reconstruction via?Cross-domain Learninges has become a pressing goal. Deep learning (DL)-based methods have proven to suppress noise-induced artifacts and promote image quality in low-dose CT imaging. However, it should be noted that most of the DL-based methods are constructed based on the CT data from a specific condition, i.e., specif作者: DOLT 時間: 2025-3-25 08:33
Enabling Geometry Aware Learning Through Differentiable Epipolar View Translationons between multiple views of the same scene, motion artifacts can be minimized, the effects of beam hardening can be reduced, and segmentation masks can be refined. In this work, we explore the idea of enabling deep learning models to access the known geometrical relations between views. This impli作者: 陪審團 時間: 2025-3-25 13:55
Enhance Early Diagnosis Accuracy of?Alzheimer’s Disease by?Elucidating Interactions Between Amyloid hows that the interaction between A. and tau is the gateway to understanding the etiology of AD, these two AD hallmarks are often treated as independent variables in the current state-of-the-art early diagnostic model for AD, which might be partially responsible for the issue of lacking explainabili作者: 不容置疑 時間: 2025-3-25 16:30 作者: sebaceous-gland 時間: 2025-3-25 22:25 作者: capillaries 時間: 2025-3-26 04:00 作者: 使習慣于 時間: 2025-3-26 08:13
Multi-Head Multi-Loss Model Calibrationpect of uncertainty quantification is the ability of a model to return predictions that are well-aligned with the actual probability of the model being correct, also known as model calibration. Although many methods have been proposed to improve calibration, no technique can match the simple, but ex作者: connoisseur 時間: 2025-3-26 09:01
Scale Federated Learning for?Label Set Mismatch in?Medical Image Classificationl collaboratively without privacy leakage. However, most previous studies have assumed that every client holds an identical label set. In reality, medical specialists tend to annotate only diseases within their area of expertise or interest. This implies that label sets in each client can be differe作者: AVERT 時間: 2025-3-26 12:46
Cross-Modulated Few-Shot Image Generation for?Colorectal Tissue Classificationcancer tissues. Our few-shot generation method, named XM-GAN, takes one base and a pair of reference tissue images as input and generates high-quality yet diverse images. Within our XM-GAN, a novel controllable fusion block densely aggregates local regions of reference images based on their similari作者: 斜坡 時間: 2025-3-26 20:50
Bidirectional Mapping with?Contrastive Learning on?Multimodal Neuroimaging Datantial biomarkers for different clinical phenotypes and brain diseases. However, most existing studies focus on one-way mapping, either projecting brain function to brain structure or inversely. This type of unidirectional mapping approach is limited by the fact that it treats the mapping as a one-wa作者: majestic 時間: 2025-3-27 00:06 作者: 真實的你 時間: 2025-3-27 01:08
Co-assistant Networks for?Label Correctionght significantly deteriorate the performance of deep neural networks (DNNs), which have been widely applied to medical image analysis. To alleviate this issue, in this paper, we propose a novel framework, namely Co-assistant Networks for Label Correction (CNLC), to simultaneously detect and correct作者: 葡萄糖 時間: 2025-3-27 05:55
M3D-NCA: Robust 3D Segmentation with?Built-In Quality Controlch models is limited by their high computational requirements, which makes them impractical for resource-constrained environments such as primary care facilities and conflict zones. Furthermore, shifts in the imaging domain can render these models ineffective and even compromise patient safety if su作者: 的是兄弟 時間: 2025-3-27 13:03
The Role of?Subgroup Separability in?Group-Fair Medical Image Classificationtantially across medical imaging modalities and protected characteristics; crucially, we show that this property is predictive of algorithmic bias. Through theoretical analysis and extensive empirical evaluation (Code is available at .), we find a relationship between subgroup separability, subgroup作者: CHOKE 時間: 2025-3-27 13:48
Conference proceedings 2023rnational Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023..The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the followin作者: 有罪 時間: 2025-3-27 18:05
Pre-trained Diffusion Models for?Plug-and-Play Medical Image Enhancementn low-dose CT and heart MR datasets demonstrate that the proposed method is versatile and robust for image denoising and super-resolution. We believe our work constitutes a practical and versatile solution to scalable and generalizable image enhancement.作者: Triglyceride 時間: 2025-3-27 22:15 作者: Spinal-Fusion 時間: 2025-3-28 06:02 作者: epidermis 時間: 2025-3-28 10:14
Chest X-ray Image Classification: A Causal Perspectiveate the influence of confounding factors on the learning of genuine causality. Experimental results demonstrate that our proposed method surpasses the performance of two open-source datasets in terms of classification performance. To access the source code for our approach, please visit: ..作者: evaculate 時間: 2025-3-28 14:00
Toward Fairness Through Fair Multi-Exit Framework for?Dermatological Disease Diagnosistance with high confidence from an internal classifier is allowed to exit early. Experimental results show that the proposed framework can improve the fairness condition over the state-of-the-art in two dermatological disease datasets.作者: grieve 時間: 2025-3-28 17:45
Co-assistant Networks for?Label Correctionabels. Moreover, we design a new bi-level optimization algorithm to optimize our proposed objective function. Extensive experiments on three popular medical image datasets demonstrate the superior performance of our framework over recent state-of-the-art methods. Source codes of the proposed method are available on ..作者: granite 時間: 2025-3-28 22:07
https://doi.org/10.1007/978-3-031-43898-1applied computing; life and medical sciences; computational biology; computer vision; computing methodol作者: 嘲弄 時間: 2025-3-29 00:28 作者: JEER 時間: 2025-3-29 06:40 作者: 悲觀 時間: 2025-3-29 09:14 作者: 整潔漂亮 時間: 2025-3-29 11:31
0302-9743 ical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical application978-3-031-43897-4978-3-031-43898-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 商議 時間: 2025-3-29 18:22 作者: INTER 時間: 2025-3-29 23:36 作者: 出沒 時間: 2025-3-30 00:50
Weizhi Nie,Chen Zhang,Dan Song,Yunpeng Bai,Keliang Xie,An-An Liu作者: Apoptosis 時間: 2025-3-30 04:41 作者: Adulterate 時間: 2025-3-30 11:01 作者: 變白 時間: 2025-3-30 13:46 作者: CERE 時間: 2025-3-30 16:41
Medical Image Computing and Computer Assisted Intervention – MICCAI 202326th International C作者: constellation 時間: 2025-3-30 20:47
GRACE: A Generalized and?Personalized Federated Learning Method for?Medical Imagingfrom overfitting. Simultaneously, GRACE employs a consistency-enhanced re-weighting aggregation to calibrate the uploaded gradients on the server side for better generalization. Extensive experiments on two medical image benchmarks demonstrate the superiority of our method under various GPFL setting作者: Palpitation 時間: 2025-3-31 03:53
DRMC: A Generalist Model with?Dynamic Routing for?Multi-center PET Image Synthesiserference, we introduce a novel dynamic routing strategy with cross-layer connections that routes data from different centers to different experts. Experiments show that our generalist model with dynamic routing (DRMC) exhibits excellent generalizability across centers. Code and data are available a作者: 編輯才信任 時間: 2025-3-31 05:22 作者: 拖債 時間: 2025-3-31 12:45 作者: 坦白 時間: 2025-3-31 16:59 作者: overhaul 時間: 2025-3-31 17:41 作者: Matrimony 時間: 2025-3-31 22:46 作者: labile 時間: 2025-4-1 03:14 作者: 間接 時間: 2025-4-1 09:54 作者: aristocracy 時間: 2025-4-1 11:46
Cross-Modulated Few-Shot Image Generation for?Colorectal Tissue Classificationenerated tissue images and real images only . time. Moreover, we utilize these generated images as data augmentation to address the few-shot tissue image classification task, achieving a gain of 4.4% in terms of mean accuracy over the vanilla few-shot classifier. Code: ..
