A survey on deep learning in medical image analysis

TL;DR: Biomedisa can drastically reduce both the time and human effort required to segment large images, and achieves a significant improvement over the conventional approach of densely pre-segmented slices with subsequent morphological interpolation as well as compared to segmentation tools that also consider the underlying image data.

TL;DR: This largest study to date adapted deep learning for the segmentation of kidney histologic structures across multiple stains and pathology laboratories and validated deep learning networks for the segmentsation of histologic structure on kidney biopsies and nephrectomies.

TL;DR: In this paper, the authors proposed a straightforward method that includes an aggregation mechanism in well-known deep learning models to combine features from images and clinical data, and carried out experiments to compare the models' performance with and without using this mechanism.

TL;DR: A fully convolutional neural network with attentional deep supervision for the automatic and accurate segmentation of the ultrasound images with improvement in overall segmentation accuracy is developed.

TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.

TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.