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Dermatologist-level classification of skin cancer with deep neural networks
This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
A guide to deep learning in healthcare
How these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems are described.
In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension
The CONSORT-AI extension is a new reporting guideline for clinical trials evaluating interventions with an AI component that recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which theAI intervention is integrated and providing analysis of error cases.
Two Distinct Scene-Processing Networks Connecting Vision and Memory
It is proposed that two distinct networks capture the primary functional division among scene-processing regions, between those that process visual features from the current view of a scene and those that connect information from a current scene view with a much broader temporal and spatial context.
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension
The SPIRIT-AI extension is a new reporting guideline for clinical trials protocols evaluating interventions with an AI component and its use will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.
CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
CO-Search is presented, a retriever-ranker semantic search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers during a time of crisis.
Visual scenes are categorized by function.
- Michelle R. Greene, C. Baldassano, Andre Esteva, D. Beck, Li Fei-Fei
- PsychologyJournal of experimental psychology. General
The hypothesis that scene categories reflect functions, or the possibilities for actions within a scene, is tested, suggesting instead that a scene's category may be determined by the scene's function.
Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks
This corrects the article to show that the method used to derive the H2O2 “spatially aggregating force” is based on a two-step process, not a single step, called a “shots fired” process.
A mountable toilet system for personalized health monitoring via the analysis of excreta
- Seung-min Park, D. Won, S. Gambhir
- Computer Science, MedicineNature Biomedical Engineering
- 6 April 2020
A ‘smart’ toilet that uses pressure and motion sensors, biometric identification, urinalysis strips, a computer-vision uroflowmeter and machine learning longitudinally tracks biomarkers of health and disease in the user’s urine and stool.