Pages that link to "Q28528865"
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The following pages link to Dermatologist-level classification of skin cancer with deep neural networks (Q28528865):
Displayed 50 items.
- Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework (Q33687566) (← links)
- Pathways to clinical CLARITY: volumetric analysis of irregular, soft, and heterogeneous tissues in development and disease (Q33919006) (← links)
- High-Definition Medicine (Q38608277) (← links)
- Big Data analysis to improve care for people living with serious illness: The potential to use new emerging technology in palliative care (Q38626371) (← links)
- Photoaging Mobile Apps as a Novel Opportunity for Melanoma Prevention: Pilot Study. (Q38651412) (← links)
- Research Techniques Made Simple: An Introduction to Use and Analysis of Big Data in Dermatology (Q38666139) (← links)
- Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys (Q38760605) (← links)
- Deep learning for healthcare: review, opportunities and challenges. (Q38798101) (← links)
- Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium. (Q39074123) (← links)
- Musculoskeletal injuries in gastrointestinal endoscopists: a systematic review (Q39433212) (← links)
- Computer-assisted diagnosis for skin cancer: have we been outsmarted? (Q40189589) (← links)
- The validity and reliability of remote diabetic foot ulcer assessment using mobile phone images (Q41534104) (← links)
- Playing to our human strengths to prepare medical students for the future (Q41607490) (← links)
- Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning (Q41696866) (← links)
- Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology (Q41987059) (← links)
- Deep machine learning provides state-of-the-art performance in image-based plant phenotyping (Q42271308) (← links)
- Rethinking cancer: current challenges and opportunities in cancer research (Q42360500) (← links)
- MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction (Q42696127) (← links)
- Open source machine-learning algorithms for the prediction of optimal cancer drug therapies (Q42699335) (← links)
- Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation. (Q42717888) (← links)
- Multivariate Approach for Alzheimer's Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization (Q45063846) (← links)
- An innovative robotic platform for magnetically-driven painless colonoscopy. (Q45907106) (← links)
- Big Data and Medicine - A Big Deal? (Q45942986) (← links)
- White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks. (Q45943077) (← links)
- Digital image analysis in breast pathology-from image processing techniques to artificial intelligence. (Q45943609) (← links)
- The influence of big (clinical) data and genomics on precision medicine and drug development. (Q45943634) (← links)
- Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology. (Q45944271) (← links)
- Rethinking Skin Lesion Segmentation in a Convolutional Classifier. (Q45944825) (← links)
- Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. (Q45945271) (← links)
- Machine Learning Approaches in Cardiovascular Imaging. (Q45945401) (← links)
- Precision Medicine for Heart Failure with Preserved Ejection Fraction: An Overview. (Q45947184) (← links)
- Machine learning will transform radiology significantly within the next 5 years. (Q45948078) (← links)
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (Q46124450) (← links)
- ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography (Q46734229) (← links)
- Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis (Q47094065) (← links)
- Deep learning architectures for multi-label classification of intelligent health risk prediction (Q47105662) (← links)
- Optimizing Chemical Reactions with Deep Reinforcement Learning (Q47108048) (← links)
- Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images (Q47115926) (← links)
- Leveraging uncertainty information from deep neural networks for disease detection. (Q47128167) (← links)
- The potential impact of artificial intelligence in radiology (Q47132118) (← links)
- Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders (Q47132893) (← links)
- Computational Psychiatry: Embracing Uncertainty and Focusing on Individuals, Not Averages (Q47133685) (← links)
- A nonlinear approach to identify pathological change of thyroid nodules based on statistical analysis of ultrasound RF signals (Q47141837) (← links)
- Multilayer perceptron architecture optimization using parallel computing techniques. (Q47148029) (← links)
- What can machine learning do? Workforce implications (Q47173219) (← links)
- Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods (Q47173232) (← links)
- Novel Image-Based Analysis for Reduction of Clinician-Dependent Variability in Measurement of the Corneal Ulcer Size (Q47173525) (← links)
- Near-Infrared Fluorescent Molecular Probe for Sensitive Imaging of Keloid (Q47189368) (← links)
- Automatic Radiographic Position Recognition from Image Frequency and Intensity (Q47194337) (← links)
- Implementation of Enterprise Imaging Strategy at a Chinese Tertiary Hospital (Q47199568) (← links)