Fully Automated Echocardiogram Interpretation in Clinical Practice

TL;DR: In this paper , three neural networks including DenseNet, EfficientNet, and ResNet are introduced for cardiac area calculation, and the best model with a mean dice accuracy of 0.91 was proposed.

TL;DR: In this article , three neural networks including DenseNet, EfficientNet, and ResNet are introduced for cardiac area calculation, and the best model with a mean dice accuracy of 0.91 was proposed.

TL;DR: In this paper , the authors used a specialized annotation tool for segmentation of the parasternal long axis (PLAX) ultrasound view of the heart, which is essential for automating the many clinical measurements performed in this view.

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: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.