Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer: Helping Artificial Intelligence Be Seen.
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Summary
A clear path toward use of AI not to replace physicians, but rather to perform simple, cost-effective, and widely available examinations and analyses that could help identify at-risk patients who require referral for specialty care while reassuring other patients that potential retinal manifestations of their diabetes are not present or are stable is established.
- Type
- letter
- Published
- 2017-12-12
- Cited by
- 214
- References
- 14
- OpenAlex
- https://openalex.org/W2774292910
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10079396
Keywords
Medicine, Otorhinolaryngology, Family medicine, Psychiatry
References
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- Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning
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- Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
- High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks
- Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
- Dermatologist–level classification of skin cancer with deep neural networks
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
- Deep Learning
Cited by
- Fighting healthcare rocketing costs with value-based medicine: the case of stroke management
- Computationally-Guided Development of a Stromal Inflammation Histologic Biomarker in Lung Squamous Cell Carcinoma
- Applications for deep learning in ecology
- Deep learning for detection of focal epileptiform discharges from scalp EEG recordings.
- An Interpretable and Expandable Deep Learning Diagnostic System for Multiple Ocular Diseases: Qualitative Study
- Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks
- An application of deep learning to detect process upset during pharmaceutical manufacturing using passive acoustic emissions
- Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology
- Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists.
- Artificial intelligence, machine learning and health systems
- Using permutations to assess confounding in machine learning applications for digital health.
- Implementing the DICOM Standard for Digital Pathology
- Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning
- High-performance medicine: the convergence of human and artificial intelligence
- The use of digital pathology and image analysis in clinical trials
- A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides
- Efficient multi-kernel DCNN with pixel dropout for stroke MRI segmentation
- Exploiting the Vulnerability of Deep Learning-Based Artificial Intelligence Models in Medical Imaging: Adversarial Attacks
- The Right Direction Needed to Develop White-Box Deep Learning in Radiology, Pathology, and Ophthalmology: A Short Review
- Automated detection of interictal epileptiform discharges in scalp electroencephalogram of patients with epilepsy
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