標(biāo)題: Titlebook: Resource-Efficient Medical Image Analysis; First MICCAI Worksho Xinxing Xu,Xiaomeng Li,Huazhu Fu Conference proceedings 2022 The Editor(s) [打印本頁] 作者: energy 時(shí)間: 2025-3-21 19:54
書目名稱Resource-Efficient Medical Image Analysis影響因子(影響力)
書目名稱Resource-Efficient Medical Image Analysis影響因子(影響力)學(xué)科排名
書目名稱Resource-Efficient Medical Image Analysis網(wǎng)絡(luò)公開度
書目名稱Resource-Efficient Medical Image Analysis網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Resource-Efficient Medical Image Analysis被引頻次
書目名稱Resource-Efficient Medical Image Analysis被引頻次學(xué)科排名
書目名稱Resource-Efficient Medical Image Analysis年度引用
書目名稱Resource-Efficient Medical Image Analysis年度引用學(xué)科排名
書目名稱Resource-Efficient Medical Image Analysis讀者反饋
書目名稱Resource-Efficient Medical Image Analysis讀者反饋學(xué)科排名
作者: GENRE 時(shí)間: 2025-3-21 20:25 作者: 進(jìn)步 時(shí)間: 2025-3-22 03:34 作者: Mumble 時(shí)間: 2025-3-22 07:41 作者: 能得到 時(shí)間: 2025-3-22 10:20
Achraf Ouahab,Olfa Ben-Ahmed,Christine Fernandez-Maloigne作者: EXCEL 時(shí)間: 2025-3-22 15:34 作者: 聚集 時(shí)間: 2025-3-22 17:59 作者: 憤怒歷史 時(shí)間: 2025-3-22 23:11
Rudan Xiao,Damien Ambrosetti,Xavier Descombese, wie etwa das der ?organisierten Kriminalit?t“ oder das des ?Terrorismus“ nach dem 11. September 2001 direkt auf MigrantInnen oder doch zumindest auf Bev?lkerungsgruppen, in denen MigrantInnen st?rker als in der Gesamtbev?lkerung repr?sentiert sind, z. B. MuslimInnen. Um all diese bekannten Zusamm作者: arterioles 時(shí)間: 2025-3-23 02:06 作者: circuit 時(shí)間: 2025-3-23 07:20
Linh D. Le,Huazhu Fu,Xinxing Xu,Yong Liu,Yanyu Xu,Jiawei Du,Joey T. Zhou,Rick Gohhe von wichtigen politischen und milit?rischen Einzelfragen aber nicht zu einem gemeinsamen Urteil gekommen war, sondern mehrere Dissense f?rmlich in ihrem Papier festhielt. Damit stand die Frage im Raum, wie es um die Orientierungsleistung des friedensethischen Leitbildes des gerechten Friedens übe作者: Esalate 時(shí)間: 2025-3-23 12:27 作者: 肥料 時(shí)間: 2025-3-23 16:56 作者: medieval 時(shí)間: 2025-3-23 20:32 作者: ASSET 時(shí)間: 2025-3-23 23:36
Self-supervised Antigen Detection Artificial Intelligence (SANDI), on an average of about 300–1000 annotations per cell type. By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for multiplexed imaging data.作者: 建筑師 時(shí)間: 2025-3-24 06:26
,Single Domain Generalization via?Spontaneous Amplitude Spectrum Diversification,proposed approach first converts the image into frequency domain using the Fourier transform, and then spontaneously generates diverse samples by editing the amplitude spectrum using a pool of randomization operations. The proposed approach is established upon the assumption that the high-level sema作者: 暗語 時(shí)間: 2025-3-24 07:13
,Triple-View Feature Learning for?Medical Image Segmentation,is strategy enables triple-view learning of generic medical image datasets. Bespoke overlap-based and boundary-based loss functions are tailored to the different stages of the training. The segmentation results are evaluated on four publicly available benchmark datasets including Ultrasound, CT, MRI作者: inchoate 時(shí)間: 2025-3-24 13:14 作者: Euthyroid 時(shí)間: 2025-3-24 16:10
,Leverage Supervised and?Self-supervised Pretrain Models for?Pathological Survival Analysis via?a?Sieady trained supervised and self-supervised models for pathological survival analysis. In this paper, we present a simple and low-cost joint representation tuning (JRT) to aggregate task-agnostic vision representation (supervised ImageNet pretrained models) and pathological specific feature represen作者: obeisance 時(shí)間: 2025-3-24 20:07 作者: Clumsy 時(shí)間: 2025-3-25 02:22 作者: Affiliation 時(shí)間: 2025-3-25 05:32 作者: chance 時(shí)間: 2025-3-25 08:26
0302-9743 in conjunction with MICCAI 2022, in September 2022 as a hybrid event. ..REMIA 2022 accepted 13 papers from the 19 submissions received. The workshop aims at creating a discussion on the issues for practical applications of medical imaging systems with data, label and hardware limitations..978-3-031-作者: 悶熱 時(shí)間: 2025-3-25 13:28
0302-9743 ims at creating a discussion on the issues for practical applications of medical imaging systems with data, label and hardware limitations..978-3-031-16875-8978-3-031-16876-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Omnipotent 時(shí)間: 2025-3-25 18:19
,An Efficient Defending Mechanism Against Image Attacking on?Medical Image Segmentation Models, models from attacks. Our result on several medical well-known benchmark datasets shows that the proposed defending mechanism to enhance the segmentation models is effective with high scores and better compared to other strong methods.作者: Fatten 時(shí)間: 2025-3-25 23:32 作者: Gourmet 時(shí)間: 2025-3-26 03:04
,Multi-task Semi-supervised Learning for?Vascular Network Segmentation and?Renal Cell Carcinoma Clasentation from Hematoxylin and Eosin (H &E) staining histopathological images is still a challenge due to the background complexity. Moreover, there is a lack of large manually annotated vascular network databases. In this paper, we propose a method that reduces reliance on labeled data through semi-作者: 天真 時(shí)間: 2025-3-26 05:49
Self-supervised Antigen Detection Artificial Intelligence (SANDI),cell phenotyping in multiplex images require extensive annotation workload due to the need for fully supervised training. To overcome this challenge, we develop SANDI, a self-supervised-based pipeline that learns intrinsic similarities in unlabeled cell images to mitigate the requirement for expert 作者: optic-nerve 時(shí)間: 2025-3-26 09:43 作者: surmount 時(shí)間: 2025-3-26 13:10
,Single Domain Generalization via?Spontaneous Amplitude Spectrum Diversification, realistic-yet-challenging scenario as a new research line, termed single domain generalization (single-DG), which aims to generalize a model trained on single source domain to multiple target domains. The existing single-DG approaches tried to address the problem by generating diverse samples using作者: 啟發(fā) 時(shí)間: 2025-3-26 19:23
,Triple-View Feature Learning for?Medical Image Segmentation,h labelling cost. We propose TriSegNet, a semi-supervised semantic segmentation framework. It uses . feature learning on a limited amount of labelled data and a large amount of unlabeled data. The triple-view architecture consists of three pixel-level classifiers and a low-level shared-weight learni作者: 我說不重要 時(shí)間: 2025-3-26 23:01
Classification of 4D fMRI Images Using ML, Focusing on Computational and Memory Utilization Efficie disease (AD), mild cognitive impairment (MCI) or being cognitive normal (CN). In this research, we represent Resting-State brain activity in Regions-of-Interest (ROI) by subsets of anatomical region voxels formed by segments of a whole brain bounding box Hilbert curve resulting in an average 5× few作者: 彩色 時(shí)間: 2025-3-27 01:10 作者: Terminal 時(shí)間: 2025-3-27 07:13
,Leverage Supervised and?Self-supervised Pretrain Models for?Pathological Survival Analysis via?a?Sig in pathological image analyses. Most existing studies have focused on developing more powerful pretrained models, which are increasingly unscalable for academic institutes. Very few, if any, studies have investigated how to take advantage of existing, yet heterogeneous, pretrained models for downs作者: 廢止 時(shí)間: 2025-3-27 11:06
Pathological Image Contrastive Self-supervised Learning,e in many tasks in the field of computer vision. Contrastive learning methods build pre-training weight parameters by crafting positive/negative samples and optimizing their distance in the feature space. It is easy to construct positive/negative samples on natural images, but the methods cannot dir作者: 我不怕犧牲 時(shí)間: 2025-3-27 14:27 作者: 刺穿 時(shí)間: 2025-3-27 17:51 作者: 建筑師 時(shí)間: 2025-3-28 00:46
,A Self-attentive Meta-learning Approach for?Image-Based Few-Shot Disease Detection,er medical knowledge from common diseases to low prevalence cases using meta-learning. Indeed, compared to natural images, medical images vary less diversely from one image to another, with complex patterns and less semantic information. Hence, extracting clinically relevant features and learning a 作者: 矛盾 時(shí)間: 2025-3-28 04:14
,Facing Annotation Redundancy: OCT Layer Segmentation with?only?10 Annotated Pixels per?Layer,for improved accuracy is driving the use of increasingly large dataset with fully pixel-level layer annotations. But the manual annotation process is expensive and tedious, further, the annotators also need sufficient medical knowledge which brings a great burden on the doctors. We observe that ther作者: phytochemicals 時(shí)間: 2025-3-28 10:04 作者: 四指套 時(shí)間: 2025-3-28 11:38
Pathological Image Contrastive Self-supervised Learning,earning on histopathological images. Results on the PatchCamelyon benchmark show that our method can improve model accuracy up to 6% while reducing the training costs, as well as reducing reliance on labeled data.作者: 破譯 時(shí)間: 2025-3-28 15:43
,Masked Video Modeling with?Correlation-Aware Contrastive Learning for?Breast Cancer Diagnosis in?Uloped to facilitate the identifying of the internal and external relationship between benign and malignant lesions. Experimental results show that our proposed approach achieved promising classification performance and can outperform other state-of-the-art methods.作者: 男生如果明白 時(shí)間: 2025-3-28 22:36 作者: 和諧 時(shí)間: 2025-3-29 02:11
https://doi.org/10.1007/978-3-031-16876-5artificial intelligence; bioinformatics; classification methods; computer networks; computer systems; com作者: 帶來 時(shí)間: 2025-3-29 07:00 作者: Tempor 時(shí)間: 2025-3-29 08:23 作者: Postulate 時(shí)間: 2025-3-29 14:28
Resource-Efficient Medical Image Analysis978-3-031-16876-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 凌辱 時(shí)間: 2025-3-29 18:22
n Banlieues, man müsse die Vorst?dte ?mit dem K?rcher“ s?ubern und die Gesellschaft auf diese Weise gewisserma?en von ihrem hartn?ckigen gesellschaftlichen Bodensatz befreien. Auch wenn sich das Unternehmen K?rcher gegen die Benutzung seines Namens aufgrund der Befürchtung eines Imageverlustes wehrt作者: amphibian 時(shí)間: 2025-3-29 22:49
Rudan Xiao,Damien Ambrosetti,Xavier Descombes Strafverfolgung geraten. Genauer w?re es dabei, nicht von MigrantInnen zu sprechen, sondern von Personen, die ein ?u?eres Ersch einungsbild der Art aufweisen, wie es mit einem Migrationshintergrund assoziiert wird. Prominentes Beispiel ist der in den Medien vielfach als Tatverd?chtiger erscheinende作者: GUILE 時(shí)間: 2025-3-30 00:44 作者: 不斷的變動(dòng) 時(shí)間: 2025-3-30 07:56
Linh D. Le,Huazhu Fu,Xinxing Xu,Yong Liu,Yanyu Xu,Jiawei Du,Joey T. Zhou,Rick Goh den Nordirak, dass die Friedensdenkschrift von 2007 ?keine ausreichende Basis zu bieten [scheint], um zu einer klaren kirchlichen Position zu finden, die in der politischen Debatte über Deutschlands aktuelle und künftige Rolle in der internationalen Sicherheitspolitik eine substantielle Orientierun
