Book ChapterDOI
Visual-Assisted Probe Movement Guidance for Obstetric Ultrasound Scanning Using Landmark Retrieval
Cheng Zhao,Richard Droste,Lior Drukker,Aris T. Papageorghiou,J. Alison Noble +4 more
- pp 670-679
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TLDR
In this paper, a Transformer-VLAD network is proposed to learn a global descriptor to represent each US image, and anchor-positive-negative US image pairs are automatically constructed through a KD-tree search of 3D probe positions.Abstract:
Automated ultrasound (US)-probe movement guidance is desirable to assist inexperienced human operators during obstetric US scanning. In this paper, we present a new visual-assisted probe movement technique using automated landmark retrieval for assistive obstetric US scanning. In a first step, a set of landmarks is constructed uniformly around a virtual 3D fetal model. Then, during obstetric scanning, a deep neural network (DNN) model locates the nearest landmark through descriptor search between the current observation and landmarks. The global position cues are visualised in real-time on a monitor to assist the human operator in probe movement. A Transformer-VLAD network is proposed to learn a global descriptor to represent each US image. This method abandons the need for deep parameter regression to enhance the generalization ability of the network. To avoid prohibitively expensive human annotation, anchor-positive-negative US image-pairs are automatically constructed through a KD-tree search of 3D probe positions. This leads to an end-to-end network trained in a self-supervised way through contrastive learning.read more
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Journal ArticleDOI
A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis
Maria Chiara Fiorentino,Francesca Pia Villani,Mariachiara Di Cosmo,Emanuele Frontoni,Sara Moccia +4 more
TL;DR: A detailed survey of the most recent work in the field can be found in this paper , with a total of 145 research papers published after 2017 and each paper is analyzed and commented on from both the methodology and application perspective.
Journal ArticleDOI
DATR: Domain-adaptive transformer for multi-domain landmark detection
TL;DR: A universal model for multi-domain landmark detection by taking advantage of transformer for modeling long dependencies and developing a domain-adaptive transformer model, named as DATR, which is trained on multiple mixed datasets from different anatomies and capable of detecting landmarks of any image from those anatomie.
Journal ArticleDOI
Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network
Ping Lu,Chenyang Wang,Jannis Hagenah,Shadi Ghiasi,Vital Consortium,Tingting Zhu,C. Louise Thwaites,David A. Clifton +7 more
TL;DR: A novel hybrid CNN-Transformer model is proposed to automatically classify tetanus severity using tetanus monitoring from low-cost wearable sensors that outperforms state-of-the-art methods in tetanus classification and finds that Random Forest with enough manually selected features can be comparable with the proposed CNN- Transformer model.
Journal ArticleDOI
Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network
TL;DR: In this article , a hybrid CNN-Transformer model was proposed to automatically classify tetanus severity using tetanus monitoring from low-cost wearable sensors, which can capture the local features from CNN and the global features from the Transformer.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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.
Proceedings Article
Attention is All you Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Lukasz Kaiser,Illia Polosukhin +7 more
TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Proceedings ArticleDOI
NetVLAD: CNN Architecture for Weakly Supervised Place Recognition
TL;DR: A convolutional neural network architecture that is trainable in an end-to-end manner directly for the place recognition task and an efficient training procedure which can be applied on very large-scale weakly labelled tasks are developed.
Posted Content
Image Transformer
Niki Parmar,Ashish Vaswani,Jakob Uszkoreit,Łukasz Kaiser,Noam Shazeer,Alexander Ku,Dustin Tran +6 more
TL;DR: In this article, a self-attention mechanism is used to attend to local neighborhoods to increase the size of images generated by the model, despite maintaining significantly larger receptive fields per layer than typical CNNs.
Proceedings Article
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy,Lucas Beyer,Alexander Kolesnikov,Dirk Weissenborn,Xiaohua Zhai,Thomas Unterthiner,Mostafa Dehghani,Matthias Minderer,Georg Heigold,Sylvain Gelly,Jakob Uszkoreit,Neil Houlsby +11 more
TL;DR: The Vision Transformer (ViT) as discussed by the authors uses a pure transformer applied directly to sequences of image patches to perform very well on image classification tasks, achieving state-of-the-art results on ImageNet, CIFAR-100, VTAB, etc.