Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features.
about
Mining textural knowledge in biological images: Applications, methods and trendsAccurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology.Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples.Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis.Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use casesA Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.Image analysis and machine learning in digital pathology: Challenges and opportunities.A neural network approach for fast, automated quantification of DIR performance.MRI-based prostate cancer detection with high-level representation and hierarchical classification.Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers.Metastasis detection from whole slide images using local features and random forests.Automatic extraction of cell nuclei from H&E-stained histopathological images.Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks.White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks.Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study.Deep learning based tissue analysis predicts outcome in colorectal cancer.A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images.A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection.Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.High-throughput ovarian follicle counting by an innovative deep learning approachImitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images
P2860
Q28077947-12A92A07-A2C1-449A-94E6-0C23DB37DF7CQ33572428-E1A83B06-368C-4D42-A74A-5E3AB9B2C6B4Q34534254-47D3502B-B6B6-4838-B39F-B646774BA857Q36149520-6291F5AF-5B24-4D31-8795-4403CBF06AF3Q36925087-CD2D4103-B1BE-429F-8575-BF1ABDCEB0EFQ37161047-C9E6E733-37D2-4EF4-8C7F-D0BAEDF52AB3Q37616337-00BA6127-0F3E-41B2-92AF-F97E26441DC2Q38379203-FD656214-7D25-496D-8C32-67893D2444BEQ38801042-E959A574-8097-450B-8BFB-51A58D87FA9AQ39009656-D772E3ED-C509-4245-A038-76C0B38B46B8Q39407913-91E2E47C-49BA-4292-A17A-3757DB26BF0DQ40443376-5E296BBF-5828-4F00-A667-1D035678306FQ40462406-61489B51-F1CB-4DD7-B3DB-AE703D1E2AC6Q40481542-AF8A7851-3CA9-414B-B7B6-8217ED5F4768Q41528825-B0B967CD-D33B-4B4B-A097-490905F03486Q45943077-F3318450-F4D0-459B-911A-03026629D6F7Q47423083-7AE257D8-D2D2-4538-88D8-97CBA212AADAQ50332434-304344FC-887E-4AB4-8787-C22D1913E640Q54943238-0721CD37-17BA-4C1C-866F-90B129B8C302Q54950331-EC15FB19-C1AA-4256-A4D3-8C6A079C4BE4Q55085452-1A19EAC7-5B57-4637-A703-227611DB0919Q55174534-0110252C-CF48-407C-AFC3-15E48D5D942CQ55185841-C1A0CED8-3459-4F7B-975C-8BA5D5B3ED9FQ55303145-34E0C30F-A9BD-4FE6-9DAE-710E1E37F96DQ58756082-24940737-D7A1-450C-B7F2-58C0FCA72148Q59137279-44A661F8-BECD-4BB4-9D8B-95D7DFF2432D
P2860
Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features.
description
2014 nî lūn-bûn
@nan
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
2014年论文
@zh
2014年论文
@zh-cn
name
Mitosis detection in breast ca ...... ional neural network features.
@ast
Mitosis detection in breast ca ...... ional neural network features.
@en
type
label
Mitosis detection in breast ca ...... ional neural network features.
@ast
Mitosis detection in breast ca ...... ional neural network features.
@en
prefLabel
Mitosis detection in breast ca ...... ional neural network features.
@ast
Mitosis detection in breast ca ...... ional neural network features.
@en
P2093
P2860
P356
P1476
Mitosis detection in breast ca ...... ional neural network features.
@en
P2093
Ajay Basavanhally
Fabio Gonzalez
Haibo Wang
Hannah Gilmore
John Tomaszewski
Mike Feldman
Natalie Shih
P2860
P304
P356
10.1117/1.JMI.1.3.034003
P577
2014-10-10T00:00:00Z