相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Hyuna Sung et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2021)
Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review
Rasheed Omobolaji Alabi et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE (2021)
Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach
Valeria Romeo et al.
ANTICANCER RESEARCH (2020)
Development and validation of a multivariable prediction model for the identification of occult lymph node metastasis in oral squamous cell carcinoma
Maxime Mermod et al.
HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK (2020)
Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning
Songhui Diao et al.
AMERICAN JOURNAL OF PATHOLOGY (2020)
Automated oral squamous cell carcinoma identification using shape, texture and color features of whole image strips
Tabassum Yesmin Rahman et al.
TISSUE & CELL (2020)
Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks
Daniel Clymer et al.
AMERICAN JOURNAL OF PATHOLOGY (2020)
Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network
Navarun Das et al.
NEURAL NETWORKS (2020)
The Effect of Image Resolution on Deep Learning in Radiography
Carl F. Sabottke et al.
RADIOLOGY-ARTIFICIAL INTELLIGENCE (2020)
Selecting training sets for support vector machines: a review
Jakub Nalepa et al.
ARTIFICIAL INTELLIGENCE REVIEW (2019)
Pathology Image Analysis Using Segmentation Deep Learning Algorithms
Shidan Wang et al.
AMERICAN JOURNAL OF PATHOLOGY (2019)
Detection and Classification of Novel Renal Histologic Phenotypes Using Deep Neural Networks
Susan Sheehan et al.
AMERICAN JOURNAL OF PATHOLOGY (2019)
Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach
Hoa Hoang Ngoc Pham et al.
AMERICAN JOURNAL OF PATHOLOGY (2019)
Deep learning-based classification of mesothelioma improves prediction of patient outcome
Pierre Courtiol et al.
NATURE MEDICINE (2019)
Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm
Pandia Rajan Jeyaraj et al.
JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY (2019)
Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence
Yoshiko Ariji et al.
ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY (2019)
Pathologist-level interpretable whole-slide cancer diagnosis with deep learning
Zizhao Zhang et al.
NATURE MACHINE INTELLIGENCE (2019)
Textural pattern classification for oral squamous cell carcinoma
T. Y. Rahman et al.
JOURNAL OF MICROSCOPY (2018)
Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images
Pegah Khosravi et al.
EBIOMEDICINE (2018)
Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images Using Deep Learning
P. Ocampo et al.
JOURNAL OF THORACIC ONCOLOGY (2018)
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Babak Ehteshami Bejnordi et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2017)
Learning from imbalanced data: open challenges and future directions
Bartosz Krawczyk
PROGRESS IN ARTIFICIAL INTELLIGENCE (2016)
Comprehensive genomic characterization of head and neck squamous cell carcinomas
Michael S. Lawrence et al.
NATURE (2015)
Deep learning in neural networks: An overview
Juergen Schmidhuber
NEURAL NETWORKS (2015)