Alle Papers – KI in der Krebserkennung

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

Automated detection of COVID-19 cases using deep neural networks with X-ray images

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Laser Capture Microdissection

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Mammographic Density and the Risk and Detection of Breast Cancer

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

Current Methods in Medical Image Segmentation

MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

Opportunities and obstacles for deep learning in biology and medicine

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

The practical implementation of artificial intelligence technologies in medicine

Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation

Segment anything in medical images

Histopathological Image Analysis: A Review

Computed Tomographic Virtual Colonoscopy to Screen for Colorectal Neoplasia in Asymptomatic Adults

U-Net: deep learning for cell counting, detection, and morphometry

Deep Learning for Health Informatics

Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening

Deformable models in medical image analysis: a survey

Dataset of breast ultrasound images

GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications