automatically

Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation

보이지 않는 딥 러닝 인공 지능 패러다임을 사용하여 다양한 유리 바닥 불투명도를 사용한 COVID-19 폐 컴퓨터 단층 촬영 분할에 대한 다기관 연구: COVLIAS 1.0 검증

Generalisable 3D printing error detection and correction via multi-head neural networks

Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy

Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images

X-Ray 이미지에서 MLES-Net 딥 러닝 모델을 통해 COVID-19 환자 감지

Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features

딥 러닝 기반 추출 기능을 사용하여 CT 스캔에서 신종 코로나바이러스 및 일반적인 폐렴 감지

BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria

BioAutoML: 박테리아의 비코딩 RNA를 예측하기 위한 자동화된 기능 엔지니어링 및 금속 학습

Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID

의미 검색 엔진 preVIEW의 지속적인 개발: COVID-19에서 긴 COVID까지

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study