Attention gated networks: Learning to leverage salient regions in medical images.
Jo Schlemper,Ozan Oktay,Michiel Schaap,Mattias P. Heinrich,Bernhard Kainz,Ben Glocker,Daniel Rueckert +6 more
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TLDR
Experimental results show that AG models consistently improve the prediction performance of the base architectures across different datasets and training sizes while preserving computational efficiency.About:
This article is published in Medical Image Analysis.The article was published on 2019-02-05 and is currently open access. It has received 966 citations till now. The article focuses on the topics: Convolutional neural network & Contextual image classification.read more
Citations
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Journal ArticleDOI
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
TL;DR: Li et al. as discussed by the authors proposed a COVID-19 Lung Infection Segmentation Deep Network ( Inf-Net) to automatically identify infected regions from chest CT slices, where a parallel partial decoder is used to aggregate the high-level features and generate a global map.
Journal ArticleDOI
Explainable Machine Learning for Scientific Insights and Discoveries
TL;DR: In this paper, the authors provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas.
Journal ArticleDOI
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications
TL;DR: A narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends, and discusses the many innovations that have advanced in deep learning and how these tools facilitate U-nets.
Journal ArticleDOI
Deep semantic segmentation of natural and medical images: a review
TL;DR: This review categorizes the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis- based, loss function-based, sequenced models, weakly supervised, and multi-task methods.
Book ChapterDOI
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
Yundong Zhang,Huiye Liu,Qiang Hu +2 more
TL;DR: TransFuse as discussed by the authors combines Transformers and CNNs in a parallel style, where both global dependency and low-level spatial details can be efficiently captured in a much shallower manner.
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.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Automatic differentiation in PyTorch
Adam Paszke,Sam Gross,Soumith Chintala,Gregory Chanan,Edward Z. Yang,Zachary DeVito,Zeming Lin,Alban Desmaison,Luca Antiga,Adam Lerer +9 more
TL;DR: An automatic differentiation module of PyTorch is described — a library designed to enable rapid research on machine learning models that focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead.
Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Journal ArticleDOI
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.