Alle Papers – KI in der Krebserkennung
165.076 Papers insgesamt · Seite 6 von 400
Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
Breast cancer screening with mammography: overview of Swedish randomised trials
Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks
COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images
The Importance of Skip Connections in Biomedical Image Segmentation
Sample size estimation in diagnostic test studies of biomedical informatics
The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges
Large scale deep learning for computer aided detection of mammographic lesions
Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)
Quantitative Analysis of Histological Staining and Fluorescence Using ImageJ
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Breast cancer histopathological image classification using Convolutional Neural Networks
History of artificial intelligence in medicine
Recent advances and clinical applications of deep learning in medical image analysis
Gland segmentation in colon histology images: The glas challenge contest
Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.
Use of active shape models for locating structures in medical images
PH<sup>2</sup> - A dermoscopic image database for research and benchmarking
A review of medical image data augmentation techniques for deep learning applications
A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
Towards a general-purpose foundation model for computational pathology
Applications of machine learning in cancer prediction and prognosis.
Machine Learning Methods for Histopathological Image Analysis
<scp>eD</scp>octor: machine learning and the future of medicine