標題: Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee [打印本頁] 作者: 過分愛國主義 時間: 2025-3-21 16:58
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022影響因子(影響力)
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022影響因子(影響力)學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022網(wǎng)絡(luò)公開度
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022網(wǎng)絡(luò)公開度學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022被引頻次
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022被引頻次學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022年度引用
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022年度引用學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022讀者反饋
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022讀者反饋學科排名
作者: dry-eye 時間: 2025-3-21 22:12 作者: 神經(jīng) 時間: 2025-3-22 02:29
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022978-3-031-16434-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 浪費物質(zhì) 時間: 2025-3-22 06:45 作者: 狂熱語言 時間: 2025-3-22 09:55
DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classificationerative feature space refinement strategy so that in the final feature space the positive and negative instances can be easily separated. Experiments on the CAMELYON16 dataset and the TCGA Lung Cancer dataset show that our method achieves new SOTA for both global classification and positive patch localization tasks.作者: restrain 時間: 2025-3-22 15:28 作者: white-matter 時間: 2025-3-22 18:17 作者: BET 時間: 2025-3-22 22:16 作者: HILAR 時間: 2025-3-23 05:22 作者: 妨礙議事 時間: 2025-3-23 05:41 作者: hypotension 時間: 2025-3-23 12:54 作者: 機構(gòu) 時間: 2025-3-23 15:18 作者: accomplishment 時間: 2025-3-23 20:16
S,R: Self-supervised Spectral Regression for?Hyperspectral Histopathology Image Classificationd is restored by using the learned coefficients to reweight the remaining bands. Two pre-text tasks are designed: (1) S.R-CR, which regresses the linear coefficients, so that the pre-trained model understands the inherent structures of HSIs and the pathological characteristics of different morpholog作者: 廚師 時間: 2025-3-24 00:51
Distilling Knowledge from?Topological Representations for?Pathological Complete Response Predictionrior performance by increasing the accuracy from previously 85.1% to 90.5% in the pCR prediction and reducing the topological computation time by about 66% on a public dataset for breast DCE-MRI images.作者: 世俗 時間: 2025-3-24 02:26
SETMIL: Spatial Encoding Transformer-Based Multiple Instance Learning for?Pathological Image Analysincoding design in the aggregating module further improves the context-information-encoding ability of SETMIL. (4) SETMIL designs a transformer-based pyramid multi-scale fusion module to comprehensively encode the information with different granularity using multi-scale receptive fields and make the 作者: charisma 時間: 2025-3-24 06:42
Clinical-Realistic Annotation for?Histopathology Images with?Probabilistic Semi-supervision: A Worstkle the challenge, we 1) proposed a different annotation strategy to image data with different levels of disease severity, 2) combined semi- and self-supervised representation learning with probabilistic weakly supervision to make use of the proposed annotations, and 3) illustrated its effectiveness作者: blackout 時間: 2025-3-24 14:46
End-to-End Learning for?Image-Based Detection of?Molecular Alterations in?Digital Pathologycer cases from The Cancer Genome Atlas. Results reach AUC scores of up to 94% and are shown to be competitive with state of the art two-stage pipelines. We believe our approach can facilitate future research in digital pathology and contribute to solve a large range of problems around the prediction作者: HILAR 時間: 2025-3-24 18:52
S5CL: Unifying Fully-Supervised, Self-supervised, and?Semi-supervised Learning Through Hierarchical rk on two public histopathological datasets show strong improvements in the case of sparse labels: for a H &E-stained colorectal cancer dataset, the accuracy increases by up to . compared to supervised cross-entropy loss; for a highly imbalanced dataset of single white blood cells from leukemia pati作者: Left-Atrium 時間: 2025-3-24 21:58 作者: 審問 時間: 2025-3-25 01:41 作者: 肥料 時間: 2025-3-25 07:15 作者: 大氣層 時間: 2025-3-25 09:34 作者: BOOST 時間: 2025-3-25 13:00
GradMix for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasetsployed two datasets to evaluate the effectiveness of GradMix. The experimental results suggest that GradMix is able to improve the performance of nuclei segmentation and classification in imbalanced pathology image datasets.作者: 熱心助人 時間: 2025-3-25 19:33
Spatial-Hierarchical Graph Neural Network with?Dynamic Structure Learning for?Histological Image Claact rich and discriminative features by mining the spatial features of different entities via graph convolutions and aggregating the semantic of multi-level entities via a vision transformer (ViT) based interaction mechanism. We evaluate the proposed framework on our collected colorectal cancer stag作者: 雀斑 時間: 2025-3-25 23:00 作者: insincerity 時間: 2025-3-26 01:24 作者: Commodious 時間: 2025-3-26 06:54
Yu Zhao,Zhenyu Lin,Kai Sun,Yidan Zhang,Junzhou Huang,Liansheng Wang,Jianhua Yaotrategien konnten die individuellen Erfahrungen und Erlebnisschichtungen nutzbar gemacht werden, um die subjektiven Orientierungsstrategien der befragten Person978-3-658-37044-2978-3-658-37045-9Series ISSN 2512-1081 Series E-ISSN 2512-109X 作者: dictator 時間: 2025-3-26 10:03
Marvin Teichmann,Andre Aichert,Hanibal Bohnenberger,Philipp Str?bel,Tobias Heimannal attention is given to?the methods of linear and nonlinear regression.?The high level tool Matlab/Octave is used to develop computational?code for micro controllers. The codes and data files for the book are available on Github and on Springer Link..The Target Groups.Students in electrical and mechan978-3-658-37210-1978-3-658-37211-8作者: Ptsd429 時間: 2025-3-26 15:17
Marin Scalbert,Maria Vakalopoulou,Florent Couzinié-Devy retailers in particular can use them to better adapt their offers to consumer needs and optimize consumers’ online shopping experience..978-3-658-37661-1978-3-658-37662-8Series ISSN 2626-3327 Series E-ISSN 2626-3335 作者: Ergots 時間: 2025-3-26 17:42
Yang Hu,Korsuk Sirinukunwattana,Kezia Gaitskell,Ruby Wood,Clare Verrill,Jens Rittscher retailers in particular can use them to better adapt their offers to consumer needs and optimize consumers’ online shopping experience..978-3-658-37661-1978-3-658-37662-8Series ISSN 2626-3327 Series E-ISSN 2626-3335 作者: eucalyptus 時間: 2025-3-26 22:02 作者: Yag-Capsulotomy 時間: 2025-3-27 01:54
l kann das ?Crisis Resource Management“ der Verkehrsluftfahrt und der Notfallmedizin sein –, gibt es in Dienstleistungsbereichen inkl. der Finanzindustrie aufgrund der regulatorischen Rahmenbedingungen einen Fokus auf Definitionen von Arbeitsanweisungen statt auf ein Training für den ?seltenen, aber作者: defuse 時間: 2025-3-27 08:23
Qiangguo Jin,Hui Cui,Changming Sun,Jiangbin Zheng,Leyi Wei,Zhenyu Fang,Zhaopeng Meng,Ran Suelen aber Abh?ngigkeiten in verknüpften IT-Systemen und noch mehr deren ?nderungen (durch Menschen) eine entscheidende Rolle, da es bei einem disruptiven IT-Ausfall mü?ig w?re, zwischen internem Problem und externem Angriff zu unterscheiden, wohingegen die Wiederherstellung der Betriebsf?higkeit dar作者: 閃光你我 時間: 2025-3-27 10:00 作者: Gullible 時間: 2025-3-27 15:40
Linhao Qu,Xiaoyuan Luo,Shaolei Liu,Manning Wang,Zhijian Songl kann das ?Crisis Resource Management“ der Verkehrsluftfahrt und der Notfallmedizin sein –, gibt es in Dienstleistungsbereichen inkl. der Finanzindustrie aufgrund der regulatorischen Rahmenbedingungen einen Fokus auf Definitionen von Arbeitsanweisungen statt auf ein Training für den ?seltenen, aber作者: 徹底檢查 時間: 2025-3-27 18:03
Jiawei Yang,Hanbo Chen,Yu Zhao,Fan Yang,Yao Zhang,Lei He,Jianhua Yaoformen, die in der Regel erhebliche wirtschaftliche Nachteile haben, Tragf?higkeitsvorteile aufweisen. Mit dem neuen System zur Profilgenerierung ist nicht nur das volle Potenzial evolventisch basierter Zahnwellenverbindungen erschlossen, sondern zudem deren Anpassung an die an sie gestellten Anford作者: 不能強迫我 時間: 2025-3-27 23:03
Shiyi Du,Qicheng Lao,Qingbo Kang,Yiyue Li,Zekun Jiang,Yanfeng Zhao,Kang Linhand einer biographieanalytischen Untersuchung und des zum Einsatz gekommenen Instrumentariums zurRekonstruktion subjektiver Orientierungsstrategien konnten die individuellen Erfahrungen und Erlebnisschichtungen nutzbar gemacht werden, um die subjektiven Orientierungsstrategien der befragten Person作者: iodides 時間: 2025-3-28 03:11
Ziyue Xu,Andriy Myronenko,Dong Yang,Holger R. Roth,Can Zhao,Xiaosong Wang,Daguang Xuattention is given to?the methods of linear and nonlinear regression.?The high level tool Matlab/Octave is used to develop computational?code for micro controllers. The codes and data files for the book are available on Github and on Springer Link..The Target Groups.Students in electrical and mechan作者: 珠寶 時間: 2025-3-28 08:48 作者: 劇毒 時間: 2025-3-28 12:01 作者: Mercantile 時間: 2025-3-28 14:38 作者: 內(nèi)疚 時間: 2025-3-28 20:01 作者: 內(nèi)疚 時間: 2025-3-29 01:09
Ziniu Qian,Kailu Li,Maode Lai,Eric I-Chao Chang,Bingzheng Wei,Yubo Fan,Yan Xuund ist seither eng mit den Entwicklungen in der au?erschulischen Kinder- und Jugendf?rderung verknüpft. Im Verst?ndnis der Soziokulturellen Animation sollen Kinder und Jugendliche die M?glichkeit haben, ihre Lebenswelt durch aktive Partizipation mitzugestalten, ihre Ideen umzusetzen und eigenkultur作者: paleolithic 時間: 2025-3-29 06:15
Tan Nhu Nhat Doan,Kyungeun Kim,Boram Song,Jin Tae Kwakf?rderung sind für die OKJA damit bedeutsame Ans?tze und k?nnen für die Konzeptentwicklung und die Handlungspraxis wesentliche Impulse leisten. Im Lichte der Grunds?tze von OKJA, wie Freiwilligkeit und Partizipation, sind Formen der Medienbildung und Medienkompetenzf?rderung allerdings immer aufgrun作者: 脊椎動物 時間: 2025-3-29 07:29 作者: custody 時間: 2025-3-29 11:43 作者: 內(nèi)部 時間: 2025-3-29 19:02
Wentai Hou,Helong Huang,Qiong Peng,Rongshan Yu,Lequan Yu,Liansheng Wangahl mit dem Scoring-Modell.Kinderzimmereinrichtung mit der Netzplantechnik.Babyphone-Kauf mit der Two-step Clusteranalyse.Text Mining von Babyratgebern.Windelbestandsmanagement.Make-or-Buy Babybrei.N?chtliches 978-3-658-37816-5作者: 詳細目錄 時間: 2025-3-29 23:30 作者: 共同給與 時間: 2025-3-30 00:44
Federated Stain Normalization for Computational Pathologyy for computational pathology. However, deep federated learning is an opportunity to create datasets that reflect the data diversity of many laboratories. Further, the effort of dataset construction can be divided among many. Unfortunately, existing algorithms cannot be easily applied to computation作者: 門閂 時間: 2025-3-30 06:37
DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classificationtly model the data distribution, and instead they only learn a bag-level or instance-level decision boundary discriminatively by training a classifier. In this paper, we propose DGMIL: a feature distribution guided deep MIL framework for WSI classification and positive patch localization. Instead of作者: exorbitant 時間: 2025-3-30 08:37
ReMix: A General and?Efficient Framework for?Multiple Instance Learning Based Whole Slide Image Clasimages and slide-level labels. Yet the decent performance of deep learning comes from harnessing massive datasets and diverse samples, urging the need for efficient training pipelines for scaling to large datasets and data augmentation techniques for diversifying samples. However, current MIL-based 作者: Repetitions 時間: 2025-3-30 13:55
S,R: Self-supervised Spectral Regression for?Hyperspectral Histopathology Image Classificationications such as computational pathology. But, the lack of adequate annotated data and the high spatiospectral dimensions of HSIs usually make classification networks prone to overfit. Thus, learning a general representation which can be transferred to the downstream tasks is imperative. To our know作者: 柱廊 時間: 2025-3-30 20:27
Distilling Knowledge from?Topological Representations for?Pathological Complete Response Predictionicator for both personalized treatment and prognosis. Current prevailing approaches for pCR prediction either require complex feature engineering or employ sophisticated topological computation, which are not efficient while yielding limited performance boosts. In this paper, we present a simple yet作者: 手勢 時間: 2025-3-30 21:53 作者: 陰郁 時間: 2025-3-31 02:12
Clinical-Realistic Annotation for?Histopathology Images with?Probabilistic Semi-supervision: A Worsttwo sources: localization requiring high expertise, and delineation requiring tedious and time-consuming work. Existing methods of easing the annotation effort mostly focus on the latter one, the extreme of which is replacing the delineation with a single label for all cases. We postulate that under作者: MIRTH 時間: 2025-3-31 05:14
End-to-End Learning for?Image-Based Detection of?Molecular Alterations in?Digital Pathologystage identifies areas of interest (e.g. tumor tissue), while the second stage processes cropped tiles from these areas in a supervised fashion. During inference, a large number of tiles are combined into a unified prediction for the entire slide. A major drawback of such approaches is the requireme作者: chance 時間: 2025-3-31 11:49 作者: Offstage 時間: 2025-3-31 13:59
Sample Hardness Based Gradient Loss for?Long-Tailed Cervical Cell Detectionning a detector to detect the cancer cells in a WSI (Whole Slice Image) image captured from the TCT (Thinprep Cytology Test) specimen, head categories (e.g. normal cells and inflammatory cells) typically have a much larger number of samples than tail categories (e.g. cancer cells). Most existing sta作者: endocardium 時間: 2025-3-31 21:23
Test-Time Image-to-Image Translation Ensembling Improves Out-of-Distribution Generalization in?Histoiations, caused by the use of different protocols across medical centers (staining, scanner), can strongly harm algorithms generalization on unseen protocols. This motivates the development of new methods to limit such loss of generalization. In this paper, to enhance robustness on unseen target pro作者: Type-1-Diabetes 時間: 2025-4-1 01:04
Predicting Molecular Traits from?Tissue Morphology Through Self-interactive Multi-instance Learningwhich relies on a fixed pretrained model for feature extraction and an instance-bag classifier. We argue that such a two-step approach is not optimal at capturing both fine-grained features at tile level and global features at slide level optimal to the task. We propose a self-interactive MIL that i作者: ectropion 時間: 2025-4-1 02:10 作者: GEAR 時間: 2025-4-1 08:28
Improved Domain Generalization for?Cell Detection in?Histopathology Images via?Test-Time Stain Augmerning have achieved promising detection performance. However, the stain color variation of histopathology images acquired at different sites can deteriorate the performance of cell detection, where a cell detector trained on a source dataset may not perform well on a different target dataset. Existi作者: 羞辱 時間: 2025-4-1 14:03 作者: 單色 時間: 2025-4-1 16:59
