… generated annotations throughout the training process. Finally, to show the adaptability of
this technique to other medical imaging fields, we demonstrate its ability to iteratively segment …

… generated annotations throughout the training process. Finally, to show the adaptability of
this technique to other medical imaging fields, we demonstrate its ability to iteratively segment …

… First, the local label-filtering step in each iteration enforces a … the training annotations, which
indicates that AIDE can alleviate … By training a neural network utilizing the conventional fully …

… to ease the work of the experts in the realisation of annotated … 33] presented a method to
support the annotation of proteins, … pages related to medicine, biology, healthcare and other …

… To alleviate the common burden of manual annotation, an … Starting with very little training
data, we iteratively train a set of … Based on recent advances of deep neural network structures …

… It can be integrated into both TensorFlow and PyTorch … of neural networks are defined in
PyMIC for medical image … for training, with initial learning rate 0.001 and maximal iteration …

… training iteration, the learning thread draws a large number of samples from the central
database of annotations… model on a server in order to ease usability and shield users from the …

… in the medical domain where expert annotation is expensive … For consistency and ease of
comparison, we used the … is the snapshot taken after 360,000 training iterations. As shown in …

… use recurrent neural networks (RNNs) to learn the annotation … In the second CNN training
round (1st iteration), we fine-tune … We present an effective framework to learn, detect disease, …

… limited number of annotated volumes available for training, we … During every training iteration,
we fed as input to the network … to each optimisation iteration, in order to alleviate the other…