Journal ArticleDOI
Ultrasonic evaluation of fetal lung development using deep learning with graph
Jiangang Chen,Size Hou,Bing Lu,Minglei Yang,Feiyang Sun,Qingli Li,Tao Tan,Xuedong Deng,Gaofeng Wei +8 more
TLDR
In this paper , the authors proposed a deep learning model for automated fetal lung segmentation and measurement, which was constructed combined U-Net with Graph model and pre-trained Vgg-16 network.About:
This article is published in Displays.The article was published on 2023-04-01. It has received 0 citations till now. The article focuses on the topics: Computer science & Fetus.read more
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
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.
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LabelMe: A Database and Web-Based Tool for Image Annotation
TL;DR: In this article, a large collection of images with ground truth labels is built to be used for object detection and recognition research, such data is useful for supervised learning and quantitative evaluation.
Journal ArticleDOI
Brain tumor segmentation with Deep Neural Networks
Mohammad Havaei,Axel Davy,David Warde-Farley,Antoine Biard,Aaron Courville,Yoshua Bengio,Chris Pal,Pierre-Marc Jodoin,Hugo Larochelle +8 more
TL;DR: A fast and accurate fully automatic method for brain tumor segmentation which is competitive both in terms of accuracy and speed compared to the state of the art, and introduces a novel cascaded architecture that allows the system to more accurately model local label dependencies.
Proceedings ArticleDOI
Improving deep neural networks for LVCSR using rectified linear units and dropout
TL;DR: Modelling deep neural networks with rectified linear unit (ReLU) non-linearities with minimal human hyper-parameter tuning on a 50-hour English Broadcast News task shows an 4.2% relative improvement over a DNN trained with sigmoid units, and a 14.4% relative improved over a strong GMM/HMM system.
Journal ArticleDOI
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks
Martin Rajchl,Matthew C. H. Lee,Ozan Oktay,Konstantinos Kamnitsas,Jonathan Passerat-Palmbach,Wenjia Bai,Mellisa Damodaram,Mary A. Rutherford,Joseph V. Hajnal,Bernhard Kainz,Daniel Rueckert +10 more
TL;DR: DeepCut as discussed by the authors proposes a method to obtain pixelwise object segmentations given an image dataset labeled weak annotations, in our case bounding boxes, by training a neural network classifier from bounding box annotations.
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