no code implementations • 16 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.
no code implementations • 7 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.
no code implementations • 1 Nov 2023 • Divyanshu Mishra, He Zhao, Pramit Saha, Aris T. Papageorghiou, J. Alison Noble
To detect OOD samples in this context, the resulting model should generalise to the intra-anatomy variations while rejecting similar OOD samples.
no code implementations • 28 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.
no code implementations • 25 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.
1 code implementation • 12 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.
1 code implementation • 22 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.
no code implementations • 26 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.
1 code implementation • 27 Mar 2022 • 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
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability.
no code implementations • 12 Sep 2021 • Zeyu Fu, Jianbo Jiao, Michael Suttie, J. Alison Noble
This imaging application is characterized by large variations in data appearance and limited availability of labeled data.
1 code implementation • 31 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.
no code implementations • 14 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.
no code implementations • 28 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.
no code implementations • 29 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.
1 code implementation • 19 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.
no code implementations • 14 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.
no code implementations • 8 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.
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.
no code implementations • 28 Feb 2020 • Jianbo Jiao, Richard Droste, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble
Therefore, there is significant interest in learning representations from unlabelled raw data.
no code implementations • 22 Jan 2020 • Richard Droste, Pierre Chatelain, Lior Drukker, Harshita Sharma, Aris T. Papageorghiou, J. Alison Noble
In this paper, in contrast, we present a method to automatically discover and localize anatomical landmarks in medical images.
no code implementations • 20 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).
no code implementations • 8 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.
no code implementations • 31 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.
no code implementations • 30 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.
no code implementations • 7 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.
no code implementations • 9 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.
1 code implementation • 27 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.
1 code implementation • 5 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.
no code implementations • 3 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.
no code implementations • 5 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.
no code implementations • 17 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.
no code implementations • 3 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.
no code implementations • 12 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.
no code implementations • 14 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.