Related references
Note: Only part of the references are listed.Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization
Hang Su et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2022)
Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement
Keli Hu et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2022)
How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective
Zongda Wu et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2022)
Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation
Ailiang Qi et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2022)
A Comprehensive Survey on Graph Neural Networks
Zonghan Wu et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)
Spatial omics and multiplexed imaging to explore cancer biology
Sabrina M. Lewis et al.
NATURE METHODS (2021)
CODEX multiplexed tissue imaging with DNA-conjugated antibodies
Sarah Black et al.
NATURE PROTOCOLS (2021)
A comprehensive exploration of semantic relation extraction via pre-trained CNNs
Qing Li et al.
KNOWLEDGE-BASED SYSTEMS (2020)
The single-cell pathology landscape of breast cancer
Hartland W. Jackson et al.
NATURE (2020)
Preparing Medical Imaging Data for Machine Learning
Martin J. Willemink et al.
RADIOLOGY (2020)
CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging
Yucheng Zhang et al.
BMC MEDICAL IMAGING (2020)
Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas
Hyungjin Kim et al.
RADIOLOGY (2020)
Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer
H. Raza Ali et al.
NATURE CANCER (2020)
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications
Chao Zhang et al.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (2020)
Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH
Chee-Huat Linus Eng et al.
NATURE (2019)
A gentle introduction to deep learning in medical image processing
Andreas Maier et al.
ZEITSCHRIFT FUR MEDIZINISCHE PHYSIK (2019)
Tumor Heterogeneity Correlates with Less Immune Response and Worse Survival in Breast Cancer Patients
Kerry-Ann McDonald et al.
ANNALS OF SURGICAL ONCOLOGY (2019)
A deep survival analysis method based on ranking
Bingzhong Jing et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE (2019)
High-definition spatial transcriptomics for in situ tissue profiling
Sanja Vickovic et al.
NATURE METHODS (2019)
Use of the concordance index for predictors of censored survival data
Adam R. Brentnall et al.
STATISTICAL METHODS IN MEDICAL RESEARCH (2018)
Tumour heterogeneity and resistance to cancer therapies
Ibiayi Dagogo-Jack et al.
NATURE REVIEWS CLINICAL ONCOLOGY (2018)
DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network
Jared L. Katzman et al.
BMC MEDICAL RESEARCH METHODOLOGY (2018)
Patterns of Immune Infiltration in Breast Cancer and Their Clinical Implications: A Gene-Expression-Based Retrospective Study
H. Raza Ali et al.
PLOS MEDICINE (2016)
Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method
Jia-Ren Lin et al.
NATURE COMMUNICATIONS (2015)
Tumour heterogeneity in the clinic
Philippe L. Bedard et al.
NATURE (2013)
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
Christina Curtis et al.
NATURE (2012)
REGULARIZATION FOR COX'S PROPORTIONAL HAZARDS MODEL WITH NP-DIMENSIONALITY
Jelena Bradic et al.
ANNALS OF STATISTICS (2011)
RANDOM SURVIVAL FORESTS
Hemant Ishwaran et al.
ANNALS OF APPLIED STATISTICS (2008)
Statistics review 12: Survival analysis
V Bewick et al.
CRITICAL CARE (2004)
Share Paper
