Zusammenfassung     Beschreibung     Ansprüche     Zeichnung  

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images   [0004] 
Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy   [0004] 
A regularized deep learning approach for image deblurring   [0004] 
In Silico Labelling'' ist in der Veröffentlichung ''In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images   [0034] 
Fully convolutional networks for semantic segmentation   [0093] 
U-Net: Convolutional Networks for Biomedical Image Segmentation   [0093] 
Medical Image Computing and Computer-Assisted Intervention - MICCAI   [0093] 
Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network   [0094] 
Rethinking the Inception Architecture for Computer Vision   [0094] 
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs   [0094] 
Dermatologist-level classification of skin cancer with deep neural networks   [0094] 
U-Net: Convolutional Networks for Biomedical Image Segmentation   [0094] 
Mask R-CNN   [0094] 
Deep Watershed Transform for Instance Segmentation   [0094] 
YOLOv3: An Incremental Improvement   [0095] 
Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy   [0095] 
In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images   [0095] 
Contentaware image restoration: pushing the limits of fluorescence microscopy   [0095] 
On the Automatic Generation of Medical Imaging Reports   [0095] 
Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation   [0095] 
A survey on deep learning in medical image analysis   [0101]