標(biāo)題: Titlebook: Computational Pathology and Ophthalmic Medical Image Analysis; First International Danail Stoyanov,Zeike Taylor,Hrvoje Bogunovic Conferenc [打印本頁] 作者: TRACT 時(shí)間: 2025-3-21 18:41
書目名稱Computational Pathology and Ophthalmic Medical Image Analysis影響因子(影響力)
書目名稱Computational Pathology and Ophthalmic Medical Image Analysis影響因子(影響力)學(xué)科排名
書目名稱Computational Pathology and Ophthalmic Medical Image Analysis網(wǎng)絡(luò)公開度
書目名稱Computational Pathology and Ophthalmic Medical Image Analysis網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Pathology and Ophthalmic Medical Image Analysis被引頻次
書目名稱Computational Pathology and Ophthalmic Medical Image Analysis被引頻次學(xué)科排名
書目名稱Computational Pathology and Ophthalmic Medical Image Analysis年度引用
書目名稱Computational Pathology and Ophthalmic Medical Image Analysis年度引用學(xué)科排名
書目名稱Computational Pathology and Ophthalmic Medical Image Analysis讀者反饋
書目名稱Computational Pathology and Ophthalmic Medical Image Analysis讀者反饋學(xué)科排名
作者: 用手捏 時(shí)間: 2025-3-21 22:03 作者: 缺陷 時(shí)間: 2025-3-22 01:32 作者: 神秘 時(shí)間: 2025-3-22 06:47
Construction of a Generative Model of?H&E Stained Pathology Images of?Pancreas Tumors Conditioned by is a genetically engineered mouse model of pancreas tumor. The model represents the correlation between the value at each voxel in the MRI image of the tumor and the pathology image patches that are observed at each portion corresponds to the location of the voxel in the MRI image. The model is rep作者: 優(yōu)雅 時(shí)間: 2025-3-22 09:12 作者: phase-2-enzyme 時(shí)間: 2025-3-22 15:14
Role of Task Complexity and Training in Crowdsourced Image Annotationing holds promise to address this demand, but so far feasibility has only be shown for simple tasks and not for high-quality annotation of complex structures which is often limited by shortage of experts. Third-year medical students participated in solving two complex tasks, labeling of images and d作者: phase-2-enzyme 時(shí)間: 2025-3-22 19:42
Capturing Global Spatial Context for Accurate Cell Classification in Skin Cancer Histology cancer, calls for a better understanding of the cancer-immune interface. Computational pathology provides a unique opportunity to spatially dissect such interface on digitised pathological slides. Accurate cellular classification is a key to ensure meaningful results, but is often challenging even 作者: 纖細(xì) 時(shí)間: 2025-3-22 22:25 作者: Inelasticity 時(shí)間: 2025-3-23 05:19 作者: 失望昨天 時(shí)間: 2025-3-23 08:24
Evaluating Out-of-the-Box Methods for the Classification of Hematopoietic Cells in Images of Stainedsification: not only are the cells more densely distributed, there are also significantly more types of hematopoietic cells. So far, several attempts have been made using custom image features and prior knowledge in form of cytoplasm and nuclei segmentations or a restricted number of cell types in p作者: Sigmoidoscopy 時(shí)間: 2025-3-23 11:29
DeepCerv: Deep Neural Network for Segmentation Free Robust Cervical Cell Classificationever traditional algorithms for the same depend on accurate segmentation of cells, which in itself is an open problem. Often the algorithms are also not evaluated by considering the huge inter-observer variability in ground truth labels. We propose a new deep learning algorithm that does not depend 作者: Aboveboard 時(shí)間: 2025-3-23 14:47 作者: 直覺沒有 時(shí)間: 2025-3-23 18:44 作者: 拋射物 時(shí)間: 2025-3-23 22:21 作者: 寬大 時(shí)間: 2025-3-24 02:35 作者: 沒有貧窮 時(shí)間: 2025-3-24 08:47
Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learningr. We explored the application of deep learning techniques to detect TB in Hematoxylin and Eosin (H&E) stained slides, and used convolutional neural networks to classify image patches as containing tumor buds, tumor glands and background. As a reference standard for training we stained slides both w作者: 確定方向 時(shí)間: 2025-3-24 13:07 作者: Instantaneous 時(shí)間: 2025-3-24 18:30
Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content models. Nevertheless, accurate labeling of large-scale medical datasets is not available and poses challenging tasks for using such datasets. Predicting unknown magnification levels and standardize staining procedures is a necessary preprocessing step for using this data in retrieval and classifica作者: Concomitant 時(shí)間: 2025-3-24 19:45
https://doi.org/10.1007/978-3-662-47801-1ased on gray level or color features were trained using leave-one-out forward selection. The best colon tissue classifier was based on color texture features obtaining an average tissue precision-recall (PR) area under the curve (AUC) of 0.886 and a cancer PR-AUC of 0.950 on 20 validation WSI H&E stains.作者: 連詞 時(shí)間: 2025-3-25 00:03 作者: chandel 時(shí)間: 2025-3-25 07:14
Excited Nuclear States for Li-13 (Lithium),hat it achieves state of the art accuracy while being extremely fast. The experimental results are also demonstrated using AIndra dataset collected by us, which also captures the inter observer variability.作者: nostrum 時(shí)間: 2025-3-25 08:45 作者: Optometrist 時(shí)間: 2025-3-25 13:39
Evaluating Out-of-the-Box Methods for the Classification of Hematopoietic Cells in Images of Stainede challenging dataset and we show that while generic classical machine learning approaches cannot compete with specialized algorithms, even out-of-the-box deep learning methods already yield valuable results. Our findings indicate that automated analysis of bone marrow images becomes possible with the advent of convolutional neural networks.作者: Esalate 時(shí)間: 2025-3-25 18:46 作者: stressors 時(shí)間: 2025-3-25 23:37
Excited Nuclear States for Li-11 (Lithium),nds to the selected voxel. We trained the generators by using an MRI image and a 3D pathology image, the latter was first reconstructed from a spatial series of the 2D pathology images and was then registered to the MRI image.作者: 排他 時(shí)間: 2025-3-26 01:59
https://doi.org/10.1007/978-3-662-47801-1especially when combined with a hard-negative mining technique. Finally we report the results of an observer study aimed at investigating the correlation between pathologists at detecting TB in IHC and H&E.作者: 冷峻 時(shí)間: 2025-3-26 06:24
Excited Nuclear States for Li-7 (Lithium),ches with several magnifications. The best model, a fusion of DenseNet-based CNNs, obtained a kappa score of 0.888. The methods are also evaluated qualitatively on a set of images from biomedical journals and TCGA prostate patches.作者: GRE 時(shí)間: 2025-3-26 10:40 作者: Feigned 時(shí)間: 2025-3-26 16:13
Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learningespecially when combined with a hard-negative mining technique. Finally we report the results of an observer study aimed at investigating the correlation between pathologists at detecting TB in IHC and H&E.作者: 外形 時(shí)間: 2025-3-26 20:23
Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Contentches with several magnifications. The best model, a fusion of DenseNet-based CNNs, obtained a kappa score of 0.888. The methods are also evaluated qualitatively on a set of images from biomedical journals and TCGA prostate patches.作者: Conducive 時(shí)間: 2025-3-27 00:36
Multi-resolution Networks for Semantic Segmentation in Whole Slide Imageslution networks based on the popular ‘U-Net’ architecture, which are evaluated on a benchmark dataset for binary semantic segmentation in WSIs. The proposed methods outperform the U-Net, demonstrating superior learning and generalization capabilities.作者: 獸群 時(shí)間: 2025-3-27 04:54 作者: intricacy 時(shí)間: 2025-3-27 07:25
Role of Task Complexity and Training in Crowdsourced Image Annotationelineation of relevant image objects in breast cancer and kidney tissue. We evaluated their performance and addressed the requirements of task complexity and training phases. Our results show feasibility and a high agreement between students and experts. The training phase improved accuracy of image labeling.作者: 歪曲道理 時(shí)間: 2025-3-27 11:50 作者: Restenosis 時(shí)間: 2025-3-27 17:25
0302-9743 PAY 2018 and the 21 full papers (out of 31 submissions) presented at OMIA 2018 were carefully reviewed and selected. The COMPAY papers focus on artificial intelligence and deep learning. The OMIA papers cover various topics in the field of ophthalmic image analysis..978-3-030-00948-9978-3-030-00949-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 漂白 時(shí)間: 2025-3-27 19:22 作者: epicondylitis 時(shí)間: 2025-3-27 23:37 作者: 茁壯成長 時(shí)間: 2025-3-28 02:18 作者: 動(dòng)作謎 時(shí)間: 2025-3-28 08:25
Excited Nuclear States for Be-6 (Beryllium),elineation of relevant image objects in breast cancer and kidney tissue. We evaluated their performance and addressed the requirements of task complexity and training phases. Our results show feasibility and a high agreement between students and experts. The training phase improved accuracy of image labeling.作者: lethargy 時(shí)間: 2025-3-28 14:17
0302-9743 th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018...The 19 full papers (out of 25 submissions) presented at COM作者: Nonflammable 時(shí)間: 2025-3-28 16:52 作者: clarify 時(shí)間: 2025-3-28 20:25
Structure Instance Segmentation in Renal Tissue: A Case Study on Tubular Immune Cell Detectionly an immune cell detection. We used a dataset of renal allograft biopsies from the Radboud University Medical Centre, Nijmegen, the Netherlands. Our modified U-net reached a Dice score of 0.85 on the structure segmentation task. The F1-score of the immune cell detection was 0.33.作者: Ardent 時(shí)間: 2025-3-29 02:55 作者: Indict 時(shí)間: 2025-3-29 07:04 作者: essential-fats 時(shí)間: 2025-3-29 09:36
Excited Nuclear States for He-8 (Helium),sis. Specialists often disagree on the final diagnosis on biopsy tissue due to the complex morphological variety. Although convolutional neural networks (CNN) have advantages in extracting discriminative features in image classification, directly training a CNN on high resolution histology images is作者: Contend 時(shí)間: 2025-3-29 12:26 作者: 開始從未 時(shí)間: 2025-3-29 18:43 作者: legitimate 時(shí)間: 2025-3-29 22:24 作者: Acetabulum 時(shí)間: 2025-3-30 01:15 作者: Paradox 時(shí)間: 2025-3-30 04:29
https://doi.org/10.1007/978-3-662-47801-1e consuming, and subjective task which could be aided by automatic cancer detection. We propose an algorithm for automatic cancer detection within WSI H&E stains using a multi class colon tissue classifier based on features extracted from 5 different color representations. Approx. 32000 tissue patch作者: 厭倦嗎你 時(shí)間: 2025-3-30 11:21 作者: Talkative 時(shí)間: 2025-3-30 14:24 作者: Kidney-Failure 時(shí)間: 2025-3-30 19:45 作者: 作繭自縛 時(shí)間: 2025-3-30 20:49
Excited Nuclear States for He-10 (Helium),or a new protein in each section. In order to compare and correlate patterns of different proteins, the images have to be registered with high accuracy. The problem we want to solve is registration of gigapixel whole slide images (WSI). This presents 3 challenges: (i) Images are very large; (ii) Thi作者: TEM 時(shí)間: 2025-3-31 01:38
Excited Nuclear States for He-9 (Helium),en acoustic parameters and the microstructure of the human brain fall within the scope of our research. In order to analyze the relationship between physical properties and microstructure of the human tissue, accurate image registration is required. To observe the microstructure of the tissue, patho作者: 同音 時(shí)間: 2025-3-31 06:19
Excited Nuclear States for Li-8 (Lithium),s, with the goal of automating part of this grading. We propose a two-step approach, in which we first perform a structure segmentation and subsequently an immune cell detection. We used a dataset of renal allograft biopsies from the Radboud University Medical Centre, Nijmegen, the Netherlands. Our 作者: Leisureliness 時(shí)間: 2025-3-31 10:03 作者: Flatus 時(shí)間: 2025-3-31 13:55
https://doi.org/10.1007/978-3-662-47801-1r. We explored the application of deep learning techniques to detect TB in Hematoxylin and Eosin (H&E) stained slides, and used convolutional neural networks to classify image patches as containing tumor buds, tumor glands and background. As a reference standard for training we stained slides both w作者: 女歌星 時(shí)間: 2025-3-31 17:47 作者: CHASM 時(shí)間: 2025-3-31 21:53
Excited Nuclear States for Li-7 (Lithium), models. Nevertheless, accurate labeling of large-scale medical datasets is not available and poses challenging tasks for using such datasets. Predicting unknown magnification levels and standardize staining procedures is a necessary preprocessing step for using this data in retrieval and classifica作者: MOAT 時(shí)間: 2025-4-1 01:51
Computational Pathology and Ophthalmic Medical Image Analysis978-3-030-00949-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: monologue 時(shí)間: 2025-4-1 07:55 作者: Melatonin 時(shí)間: 2025-4-1 11:35 作者: 柔軟 時(shí)間: 2025-4-1 17:12
