Image analysis and machine learning in digital pathology: Challenges and opportunities.
about
Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide ImagesDeep Learning for Classification of Colorectal Polyps on Whole-slide Images.Machine learning for epigenetics and future medical applications.Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features.Gland segmentation in prostate histopathological images.A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome.An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival.Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.Diagnostic Accuracy of Virtual Pathology vs Traditional Microscopy in a Large Dermatopathology Study.Correlation Filters for Detection of Cellular Nuclei in Histopathology Images.Unsupervised morphological segmentation of tissue compartments in histopathological images.An Image Analysis Resource for Cancer Research: PIIP-Pathology Image Informatics Platform for Visualization, Analysis, and Management.Digital image analysis of Ki67 proliferation index in breast cancer using virtual dual staining on whole tissue sections: clinical validation and inter-platform agreement.Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection.A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM).Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer.High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection.Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care.Advances in the computational and molecular understanding of the prostate cancer cell nucleus
P2860
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P2860
Image analysis and machine learning in digital pathology: Challenges and opportunities.
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
Image analysis and machine learning in digital pathology: Challenges and opportunities.
@en
type
label
Image analysis and machine learning in digital pathology: Challenges and opportunities.
@en
prefLabel
Image analysis and machine learning in digital pathology: Challenges and opportunities.
@en
P2860
P1476
Image analysis and machine learning in digital pathology: Challenges and opportunities.
@en
P2093
Anant Madabhushi
George Lee
P2860
P304
P356
10.1016/J.MEDIA.2016.06.037
P577
2016-07-04T00:00:00Z