Search Results for author: J. Alison Noble

Found 35 papers, 8 papers with code

Explaining Explainability: Understanding Concept Activation Vectors

no code implementations4 Apr 2024 Angus Nicolson, Lisa Schut, J. Alison Noble, Yarin Gal

We introduce tools designed to detect the presence of these properties, provide insight into how they affect the derived explanations, and provide recommendations to minimise their impact.

Semi-weakly-supervised neural network training for medical image registration

no code implementations16 Feb 2024 Yiwen Li, Yunguan Fu, Iani J. M. B. Gayo, Qianye Yang, Zhe Min, Shaheer U. Saeed, Wen Yan, Yipei Wang, J. Alison Noble, Mark Emberton, Matthew J. Clarkson, Dean C. Barratt, Victor A. Prisacariu, Yipeng Hu

For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupervised losses are unavailable or ineffective.

Image Registration Medical Image Registration

Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection

no code implementations7 Feb 2024 Pramit Saha, Divyanshu Mishra, Felix Wagner, Konstantinos Kamnitsas, J. Alison Noble

Secondly, we introduce a modality imputation network (MIN) pre-trained in a multimodal client for modality translation in unimodal clients and investigate its potential towards mitigating the missing modality problem.

Federated Learning Imputation

Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data

no code implementations28 Oct 2023 Pramit Saha, Divyanshu Mishra, J. Alison Noble

The most challenging, yet practical, setting of semi-supervised federated learning (SSFL) is where a few clients have fully labeled data whereas the other clients have fully unlabeled data.

Federated Learning Image Classification

Show from Tell: Audio-Visual Modelling in Clinical Settings

no code implementations25 Oct 2023 Jianbo Jiao, Mohammad Alsharid, Lior Drukker, Aris T. Papageorghiou, Andrew Zisserman, J. Alison Noble

Auditory and visual signals usually present together and correlate with each other, not only in natural environments but also in clinical settings.

Self-Supervised Learning

Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration

1 code implementation12 Sep 2022 Yiwen Li, Yunguan Fu, Iani Gayo, Qianye Yang, Zhe Min, Shaheer Saeed, Wen Yan, Yipei Wang, J. Alison Noble, Mark Emberton, Matthew J. Clarkson, Henkjan Huisman, Dean Barratt, Victor Adrian Prisacariu, Yipeng Hu

The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations.

Few-Shot Learning Segmentation

Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound

1 code implementation22 Aug 2022 Zeyu Fu, Jianbo Jiao, Robail Yasrab, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble

The proposed approach is demonstrated for automated fetal ultrasound imaging tasks, enabling the positive pairs from the same or different ultrasound scans that are anatomically similar to be pulled together and thus improving the representation learning.

Anatomy Contrastive Learning +2

Multimodal-GuideNet: Gaze-Probe Bidirectional Guidance in Obstetric Ultrasound Scanning

no code implementations26 Jul 2022 Qianhui Men, Clare Teng, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble

To understand the causal relationship between gaze movement and probe motion, our model exploits multitask learning to jointly learn two related tasks: predicting gaze movements and probe signals that an experienced sonographer would perform in routine obstetric scanning.

Adaptable image quality assessment using meta-reinforcement learning of task amenability

1 code implementation31 Jul 2021 Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides, Zachary M. C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, J. Alison Noble, Dean C. Barratt, Yipeng Hu

Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19. 7% and 29. 6% expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100% expert labels.

Image Classification Image Quality Assessment +4

Principled Ultrasound Data Augmentation for Classification of Standard Planes

no code implementations14 Mar 2021 Lok Hin Lee, Yuan Gao, J. Alison Noble

In this paper, we present an augmentation policy search method with the goal of improving model classification performance.

Classification Data Augmentation +1

Cross-Task Representation Learning for Anatomical Landmark Detection

no code implementations28 Sep 2020 Zeyu Fu, Jianbo Jiao, Michael Suttie, J. Alison Noble

The main idea of the proposed method is to retain the feature representations of the source model on the target task data, and to leverage them as an additional source of supervisory signals for regularizing the target model learning, thereby improving its performance under limited training samples.

Face Recognition Representation Learning +1

Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination

no code implementations29 Aug 2020 Qianye Yang, Yunguan Fu, Francesco Giganti, Nooshin Ghavami, Qingchao Chen, J. Alison Noble, Tom Vercauteren, Dean Barratt, Yipeng Hu

Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program.

Image Registration Morphological Analysis +1

Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis

1 code implementation19 Aug 2020 Jianbo Jiao, Ana I. L. Namburete, Aris T. Papageorghiou, J. Alison Noble

To regularise the anatomical structures between US and MRI during synthesis, we further propose an adversarial structural constraint.

Image Generation

Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound

no code implementations14 Aug 2020 Jianbo Jiao, Yifan Cai, Mohammad Alsharid, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble

For this case, we assume that there is a high correlation between the ultrasound video and the corresponding narrative speech audio of the sonographer.

Contrastive Learning Gaze Prediction +1

Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound

no code implementations8 Jul 2020 Richard Droste, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble

Evaluations for 3 standard plane types show that the model provides a useful guidance signal with an accuracy of 88. 8% for goal prediction and 90. 9% for action prediction.

Unified Image and Video Saliency Modeling

2 code implementations ECCV 2020 Richard Droste, Jianbo Jiao, J. Alison Noble

We evaluate our method on the video saliency datasets DHF1K, Hollywood-2 and UCF-Sports, and the image saliency datasets SALICON and MIT300.

Domain Adaptation Saliency Prediction +1

Heterogeneous tissue characterization using ultrasound: a comparison of fractal analysis backscatter models on liver tumors

no code implementations20 Dec 2019 Omar S. Al-Kadi, Daniel Y. F. Chung, Constantin C. Coussios, J. Alison Noble

Performance was assessed based on 608 cross-sectional clinical ultrasound RF images of liver tumors (230 and 378 demonstrating respondent and non-respondent cases, respectively).


Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images

no code implementations8 Sep 2019 Jianbo Jiao, Ana I. L. Namburete, Aris T. Papageorghiou, J. Alison Noble

The feasibility of the approach to produce realistic looking MR images is demonstrated quantitatively and with a qualitative evaluation compared to real fetal MR images.


UPI-Net: Semantic Contour Detection in Placental Ultrasound

no code implementations31 Aug 2019 Huan Qi, Sally Collins, J. Alison Noble

In this paper, we investigate utero-placental interface (UPI) detection in 2D placental ultrasound images by formulating it as a semantic contour detection problem.

Contour Detection

Conditional Segmentation in Lieu of Image Registration

no code implementations30 Jun 2019 Yipeng Hu, Eli Gibson, Dean C. Barratt, Mark Emberton, J. Alison Noble, Tom Vercauteren

Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned.

Image Registration Image Segmentation +2

Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention

no code implementations7 Mar 2019 Richard Droste, Yifan Cai, Harshita Sharma, Pierre Chatelain, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble

Secondly, we train a simple softmax regression on the feature activations of each CNN layer in order to evaluate the representations independently of transfer learning hyper-parameters.

regression Representation Learning +2

Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration

no code implementations9 Jul 2018 Yipeng Hu, Marc Modat, Eli Gibson, Wenqi Li, Nooshin Ghavami, Ester Bonmati, Guotai Wang, Steven Bandula, Caroline M. Moore, Mark Emberton, Sébastien Ourselin, J. Alison Noble, Dean C. Barratt, Tom Vercauteren

A median target registration error of 3. 6 mm on landmark centroids and a median Dice of 0. 87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.

Image Registration

Adversarial Deformation Regularization for Training Image Registration Neural Networks

1 code implementation27 May 2018 Yipeng Hu, Eli Gibson, Nooshin Ghavami, Ester Bonmati, Caroline M. Moore, Mark Emberton, Tom Vercauteren, J. Alison Noble, Dean C. Barratt

During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation.

Image Registration

Label-driven weakly-supervised learning for multimodal deformable image registration

1 code implementation5 Nov 2017 Yipeng Hu, Marc Modat, Eli Gibson, Nooshin Ghavami, Ester Bonmati, Caroline M. Moore, Mark Emberton, J. Alison Noble, Dean C. Barratt, Tom Vercauteren

Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms.

Image Registration Weakly-supervised Learning

Ω-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks

no code implementations3 Nov 2017 Davis M. Vigneault, Weidi Xie, Carolyn Y. Ho, David A. Bluemke, J. Alison Noble

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses.

Image Segmentation Segmentation +1

Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

no code implementations5 Sep 2017 Yipeng Hu, Eli Gibson, Tom Vercauteren, Hashim U. Ahmed, Mark Emberton, Caroline M. Moore, J. Alison Noble, Dean C. Barratt

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image.


Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks

no code implementations17 Jul 2017 Yipeng Hu, Eli Gibson, Li-Lin Lee, Weidi Xie, Dean C. Barratt, Tom Vercauteren, J. Alison Noble

Sonography synthesis has a wide range of applications, including medical procedure simulation, clinical training and multimodality image registration.

Anatomy Image Registration +1

Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video

no code implementations3 Jul 2017 Weilin Huang, Christopher P. Bridge, J. Alison Noble, Andrew Zisserman

We present an automatic method to describe clinically useful information about scanning, and to guide image interpretation in ultrasound (US) videos of the fetal heart.

Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization

no code implementations12 Apr 2017 Davis M. Vigneault, Weidi Xie, David A. Bluemke, J. Alison Noble

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences.

Quantification of Ultrasonic Texture heterogeneity via Volumetric Stochastic Modeling for Tissue Characterization

no code implementations14 Jan 2016 O. S. Al-Kadi, Daniel Y. F. Chung, Robert C. Carlisle, Constantin C. Coussios, J. Alison Noble

In the case of ultrasound, the Nakagami distribution is a general model of the ultrasonic backscattering envelope under various scattering conditions and densities where it can be employed for characterizing image texture, but the subtle intra-heterogeneities within a given mass are difficult to capture via this model as it works at a single spatial scale.

Texture Classification

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