標題: Titlebook: Digital Pathology; 15th European Congre Constantino Carlos Reyes-Aldasoro,Andrew Janowczyk Conference proceedings 2019 Springer Nature Swit [打印本頁] 作者: 迅速 時間: 2025-3-21 17:16
書目名稱Digital Pathology影響因子(影響力)
書目名稱Digital Pathology影響因子(影響力)學科排名
書目名稱Digital Pathology網(wǎng)絡公開度
書目名稱Digital Pathology網(wǎng)絡公開度學科排名
書目名稱Digital Pathology被引頻次
書目名稱Digital Pathology被引頻次學科排名
書目名稱Digital Pathology年度引用
書目名稱Digital Pathology年度引用學科排名
書目名稱Digital Pathology讀者反饋
書目名稱Digital Pathology讀者反饋學科排名
作者: climax 時間: 2025-3-21 20:38
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/279586.jpg作者: Colonnade 時間: 2025-3-22 01:47 作者: Arboreal 時間: 2025-3-22 07:01
Nancy Cushing,Michael Kilmister,Nathan Scottnstruct a large pan-cancer dataset for nuclei instance segmentation and classification, with minimal sampling bias. The dataset consists of 455 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the作者: Accrue 時間: 2025-3-22 10:34 作者: committed 時間: 2025-3-22 16:49
“Ruins True Refuge”: Beckett and Pintercomputational resources has been achieved, there are still problems in WSI that need to be solved. A major challenge is the scan size. The dimensions of digitized tissue samples may exceed 100,000 by 100,000 pixels causing memory and efficiency obstacles for real-time processing. The main contributi作者: committed 時間: 2025-3-22 19:51
“Ruins True Refuge”: Beckett and Pintercial tasks in image analysis. We propose a methodology to improve segmentability of histological images by making use of image-to-image translation. We generate virtual stains and exploit the additional information during segmentation. Specifically a very basic pixel-based segmentation approach is a作者: heart-murmur 時間: 2025-3-22 22:13 作者: Lymphocyte 時間: 2025-3-23 04:32 作者: 饑荒 時間: 2025-3-23 05:48 作者: AGGER 時間: 2025-3-23 12:39 作者: 預定 時間: 2025-3-23 13:58
Nick Bassiliades,Georg Gottlob,Dumitru Romane. Tumor buds are representing an invasive pattern and are frequently investigated as a new diagnostic factor for measuring the aggressiveness of colorectal cancer. However, counting the number of buds under the microscope in a high power field by eyeballing is a strenuous, lengthy and error-prone t作者: 注射器 時間: 2025-3-23 21:18 作者: FRAUD 時間: 2025-3-23 23:27 作者: Individual 時間: 2025-3-24 02:27 作者: 開始發(fā)作 時間: 2025-3-24 08:26
https://doi.org/10.1007/978-3-030-23937-4Digital pathology; histology; machine learning; Artificial Intelligence; segmentation; image analysis; act作者: 證明無罪 時間: 2025-3-24 11:47 作者: 剝皮 時間: 2025-3-24 15:54 作者: Bouquet 時間: 2025-3-24 22:48 作者: arcane 時間: 2025-3-25 00:59
PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classificationnstruct a large pan-cancer dataset for nuclei instance segmentation and classification, with minimal sampling bias. The dataset consists of 455 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the作者: 樸素 時間: 2025-3-25 07:09 作者: ferment 時間: 2025-3-25 08:58 作者: 無彈性 時間: 2025-3-25 15:40
Virtually Redying Histological Images with Generative Adversarial Networks to Facilitate Unsupervisecial tasks in image analysis. We propose a methodology to improve segmentability of histological images by making use of image-to-image translation. We generate virtual stains and exploit the additional information during segmentation. Specifically a very basic pixel-based segmentation approach is a作者: 貪心 時間: 2025-3-25 17:46 作者: lymphoma 時間: 2025-3-25 23:28 作者: badinage 時間: 2025-3-26 03:09
Automated Segmentation of DCIS in Whole Slide Imageswe train several U-Net architectures – deep convolutional neural networks designed to output probability maps – to segment DCIS in whole slide images and validate the optimal patch field of view necessary to achieve superior accuracy at the slide-level. We showed a U-Net trained at 5x achieved the b作者: Nomadic 時間: 2025-3-26 07:29 作者: omnibus 時間: 2025-3-26 10:43 作者: Exclude 時間: 2025-3-26 15:50
Improving Prostate Cancer Detection with Breast Histopathology Imagesostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies emplo作者: 演講 時間: 2025-3-26 20:07 作者: 鞭打 時間: 2025-3-27 00:07
A Fast Pyramidal Bayesian Model for Mitosis Detection in Whole-Slide Imagestypes of cancer and specifically for the breast cancer. In whole-slide images the main goal is to detect its presence on the full image. This paper makes several contributions to the mitosis detection task in whole-slide in order to improve the current state of the art and efficiency. A new coarse t作者: 相符 時間: 2025-3-27 01:22 作者: vascular 時間: 2025-3-27 07:36 作者: collateral 時間: 2025-3-27 13:01 作者: 行為 時間: 2025-3-27 13:48
Lecture Notes in Computer Sciencesing an unsupervised deep learning approach based on CycleGAN. We also propose a method to deal with tiling artifacts caused by normalization layers and we validate our approach by comparing the results of tissue analysis algorithms for virtual and real images.作者: 燒瓶 時間: 2025-3-27 21:46
The RuleML Knowledge-Interoperation Hubcting capabilities of the model. Both architectures show comparable performance to a second expert annotator on an independent test set. This is preliminary work for a pipeline targeted at predicting recurrence risk in DCIS patients.作者: outer-ear 時間: 2025-3-28 01:19
Alexander Artikis,Matthias Weidliching a watershed algorithm based on the distance maps. Evaluated on a publicly available dataset containing images from various human organs, the proposed algorithm achieves an average aggregate Jaccard index of 56.87%, outperforming several state-of-the-art algorithms applied on the same dataset.作者: 共同生活 時間: 2025-3-28 04:53
Nick Bassiliades,Georg Gottlob,Dumitru Roman network is applied to filter and reduce the number of false positive candidates detected in the first step. By comparing the automatically detected buds with a gold standard created by manual annotations, we gain a score of 0.977 for precision and 0.934 for sensitivity in our test sets on over 8.000 tumor buds.作者: 大都市 時間: 2025-3-28 07:54
PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification the learnt knowledge can be efficiently transferred to create new datasets. All three streams are either validated on existing public benchmarks or validated by expert pathologists, and finally merged and validated once again to create a large, comprehensive pan-cancer nuclei segmentation and detection dataset PanNuke.作者: 工作 時間: 2025-3-28 11:34 作者: progestogen 時間: 2025-3-28 17:35
Automated Segmentation of DCIS in Whole Slide Imagescting capabilities of the model. Both architectures show comparable performance to a second expert annotator on an independent test set. This is preliminary work for a pipeline targeted at predicting recurrence risk in DCIS patients.作者: CLAP 時間: 2025-3-28 21:06 作者: 吹牛大王 時間: 2025-3-29 01:41
Automatic Detection of Tumor Buds in Pan-Cytokeratin Stained Colorectal Cancer Sections by a Hybrid network is applied to filter and reduce the number of false positive candidates detected in the first step. By comparing the automatically detected buds with a gold standard created by manual annotations, we gain a score of 0.977 for precision and 0.934 for sensitivity in our test sets on over 8.000 tumor buds.作者: 禍害隱伏 時間: 2025-3-29 05:05
“Ruins True Refuge”: Beckett and Pinterques. The results of this proof-of-concept trial indicate a performance gain compared to segmentation with the source stain only. Further experiments including more powerful supervised state-of-the-art machine learning approaches and larger evaluation data sets need to follow.作者: 無能性 時間: 2025-3-29 09:54 作者: Distribution 時間: 2025-3-29 15:02 作者: SCORE 時間: 2025-3-29 18:26 作者: GILD 時間: 2025-3-29 19:59 作者: 憂傷 時間: 2025-3-30 00:01 作者: Mobile 時間: 2025-3-30 04:37
0302-9743 il 2019. The 21 full papers presented in this volume were carefully reviewed and selected from 30 submissions. The congress theme will be Accelerating Clinical Deployment, with a focus on computational pathology and leveraging the power of big data and artificial intelligence to bridge the gaps betw作者: 退潮 時間: 2025-3-30 12:09 作者: 一夫一妻制 時間: 2025-3-30 13:46
Conference proceedings 2019he 21 full papers presented in this volume were carefully reviewed and selected from 30 submissions. The congress theme will be Accelerating Clinical Deployment, with a focus on computational pathology and leveraging the power of big data and artificial intelligence to bridge the gaps between resear作者: Invigorate 時間: 2025-3-30 19:46
Active Learning for Patch-Based Digital Pathology Using Convolutional Neural Networks to Reduce Annoained on small patches, each containing a single nucleus. Traditional query strategies performed worse than random sampling. A K-centre sampling strategy showed a modest gain. Further investigation is needed in order to achieve significant performance gains using deep active learning for this task.作者: 膝蓋 時間: 2025-3-30 21:41
Patch Clustering for Representation of Histopathology Imagese the same characteristics. We used a Gaussian mixture model (GMM) to represent each class with a rather small (10%–50%) portion of patches. The results showed that LBP features can outperform deep features. By selecting only 50% of all patches after SOM clustering and GMM patch selection, we receiv作者: Preserve 時間: 2025-3-31 04:11
Deep Features for Tissue-Fold Detection in Histopathology Imagesgurations. Based on the leave-one-out validation strategy, we achieved . accuracy, whereas with augmentation the accuracy increased to .. We have tested the generalization of our method with five unseen WSIs from the NIH (National Cancer Institute) dataset. The accuracy for patch-wise detection was 作者: 膽小鬼 時間: 2025-3-31 07:41 作者: mechanism 時間: 2025-3-31 11:23
Multi-tissue Partitioning for Whole Slide Images of Colorectal Cancer Histopathology Images with Dee
