Attention U-Net: Learning Where to Look for the Pancreas

Published in

arXiv.org(2018)

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TL;DR

A novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes is proposed to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs).

Abstract

We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN architectures such as the U-Net model with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed Attention U-Net architecture is evaluated on two large CT abdominal datasets for multi-class image segmentation. Experimental results show that AGs consistently improve the prediction performance of U-Net across different datasets and training sizes while preserving computational efficiency. The code for the proposed architecture is publicly available.

Authors

D. Rueckert

14 papers

Nils Y. Hammerla

2 papers

Ben Glocker

10 papers

References41 items

Learn To Pay Attention

TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions

Fully convolutional multi‐scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers

Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network

Non-local Neural Networks

Attention U-Net: Learning Where to Look for the Pancreas

Published in

arXiv.org(2018)

External Links:

Generate Graph

TL;DR

Abstract

Authors

D. Rueckert

14 papers

Nils Y. Hammerla

2 papers

Ben Glocker

10 papers

References41 items

Learn To Pay Attention

TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions

Fully convolutional multi‐scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers

Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network

Non-local Neural Networks

M. Heinrich

4 papers

Jo Schlemper

5 papers

Steven G. McDonagh

3 papers

Loic Le Folgoc

1 papers

Bernhard Kainz

2 papers

Graph Attention Networks

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Human-level CMR image analysis with deep fully convolutional networks

DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation

Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation

Towards Image-Guided Pancreas and Biliary Endoscopy: Automatic Multi-organ Segmentation on Abdominal CT with Dense Dilated Networks

Multi-label Whole Heart Segmentation Using CNNs and Anatomical Label Configurations

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Squeeze-and-Excitation Networks

Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

Bottom-Up and Top-Down Attention for Image Captioning and VQA

Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function

Attention is All you Need

Residual Attention Network for Image Classification

Hierarchical 3D fully convolutional networks for multi-organ segmentation

Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆

Learning what to look in chest X-rays with a recurrent visual attention model

A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans

Highway and Residual Networks learn Unrolled Iterative Estimation

Soft Multi-organ Shape Models via Generalized PCA: A General Framework

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

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Hierarchical Multi-Organ Segmentation Without Registration in 3D Abdominal CT Images

Effective Approaches to Attention-based Neural Machine Translation

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Holistically-Nested Edge Detection

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Fully convolutional networks for semantic segmentation

Deeply-Supervised Nets

Neural Machine Translation by Jointly Learning to Align and Translate

Recurrent Models of Visual Attention

Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation

Data from Pancreas-CT

Field of Study

Computer Science

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

arXiv.org

Type

URL

https://arxiv.org

Alternate Names

ArXiv