• Corpus ID: 4884844

Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

@article{Schlemper2018AttentionGatedNF,
  title={Attention-Gated Networks for Improving Ultrasound Scan Plane Detection},
  author={Jo Schlemper and Ozan Oktay and Liang Chen and Jacqueline Matthew and Caroline L. Knight and Bernhard Kainz and Ben Glocker and Daniel Rueckert},
  journal={ArXiv},
  year={2018},
  volume={abs/1804.05338}
}
In this work, we apply an attention-gated network to real-time automated scan plane detection for fetal ultrasound screening. [] Key Method A soft-attention mechanism generates a gating signal that is end-to-end trainable, which allows the network to contextualise local information useful for prediction. The proposed attention mechanism is generic and it can be easily incorporated into any existing classification architectures, while only requiring a few additional parameters. We show that, when the base…

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References

SHOWING 1-10 OF 36 REFERENCES

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

A novel method based on convolutional neural networks is proposed, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box while providing optimal output for the localization task.

Real-Time Detection and Localisation of Fetal Standard Scan Planes in 2D Freehand Ultrasound

A novel method is proposed which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box and is designed to operate in real-time while providing optimal output for the localisation task.

Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification

A three-branch attention guided convolution neural network (AG-CNN) that learns from disease-specific regions to avoid noise and improve alignment, and also integrates a global branch to compensate the lost discriminative cues by local branch.

TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays

A novel Text-Image Embedding network (TieNet) is proposed for extracting the distinctive image and text representations of chest X-rays and multi-level attention models are integrated into an end-to-end trainable CNN-RNN architecture for highlighting the meaningful text words and image regions.

DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification

DeepLung has performance comparable to experienced doctors both for the nodule-level and patient-level diagnosis on the LIDC-IDRI dataset and surpassed the performance of experienced doctors based on image modality.

MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network

This paper proposes MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process.

Fast and accurate view classification of echocardiograms using deep learning

A machine-learning technique is used to teach a computer to recognize different types of video and still images produced by echocardiogram tests, and it is shown that the model could correctly classify what heart anatomy was shown in videos with 98% accuracy.

Residual Attention Network for Image Classification

The proposed Residual Attention Network is a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion and can be easily scaled up to hundreds of layers.

Anatomy-specific classification of medical images using deep convolutional nets

It is demonstrated that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis and a data augmentation approach can help to enrich the data set and improve classification performance.