Search Results for author: Yipeng Hu

Found 27 papers, 6 papers with code

Few-shot Semantic Segmentation with Self-supervision from Pseudo-classes

1 code implementation22 Oct 2021 Yiwen Li, Gratianus Wesley Putra Data, Yunguan Fu, Yipeng Hu, Victor Adrian Prisacariu

Despite the success of deep learning methods for semantic segmentation, few-shot semantic segmentation remains a challenging task due to the limited training data and the generalisation requirement for unseen classes.

Few-Shot Semantic Segmentation Semantic Segmentation

Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks

no code implementations12 Oct 2021 Ester Bonmati, Yipeng Hu, Alexander Grimwood, Gavin J. Johnson, George Goodchild, Margaret G. Keane, Kurinchi Gurusamy, Brian Davidson, Matthew J. Clarkson, Stephen P. Pereira, Dean C. Barratt

In this work, we propose a multi-modal convolutional neural network (CNN) architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure.

Image Classification

Real-time multimodal image registration with partial intraoperative point-set data

no code implementations10 Sep 2021 Zachary M C Baum, Yipeng Hu, Dean C Barratt

Consisting of two modules, a global feature extraction module and a point transformation module, FPT does not assume explicit constraints based on point vicinity, thereby overcoming a common requirement of previous learning-based point-set registration methods.

Image Registration

Lung Ultrasound Segmentation and Adaptation between COVID-19 and Community-Acquired Pneumonia

no code implementations6 Aug 2021 Harry Mason, Lorenzo Cristoni, Andrew Walden, Roberto Lazzari, Thomas Pulimood, Louis Grandjean, Claudia AM Gandini Wheeler-Kingshott, Yipeng Hu, Zachary MC Baum

Furthermore, we offer a possible explanation that correlates the segmentation performance to label consistency and data domain diversity in this point-of-care lung ultrasound application.

Fine-tuning Unsupervised Domain Adaptation

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 +2

Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels

1 code implementation28 Jul 2021 Liam F Chalcroft, Jiongqi Qu, Sophie A Martin, Iani JMB Gayo, Giulio V Minore, Imraj RD Singh, Shaheer U Saeed, Qianye Yang, Zachary MC Baum, Andre Altmann, Yipeng Hu

When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels.

Learning to Address Intra-segment Misclassification in Retinal Imaging

2 code implementations25 Apr 2021 Yukun Zhou, MouCheng Xu, Yipeng Hu, Hongxiang Lin, Joseph Jacob, Pearse Keane, Daniel Alexander

Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity.

Retinal Vessel Segmentation

Learning image quality assessment by reinforcing task amenable data selection

no code implementations15 Feb 2021 Shaheer U. Saeed, Yunguan Fu, Zachary M. C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, Dean C. Barratt, Yipeng Hu

In this paper, we consider a type of image quality assessment as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classification or segmentation.

Image Classification Image Quality Assessment

Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification

no code implementations6 Oct 2020 Alexander Grimwood, Helen McNair, Yipeng Hu, Ester Bonmati, Dean Barratt, Emma Harris

For images with unanimous consensus between observers, anatomical classification accuracy was 97. 2% and probe adjustment accuracy was 94. 9%.

Classification General Classification

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

Image quality assessment for closed-loop computer-assisted lung ultrasound

no code implementations20 Aug 2020 Zachary M. C. Baum, Ester Bonmati, Lorenzo Cristoni, Andrew Walden, Ferran Prados, Baris Kanber, Dean C. Barratt, David J. Hawkes, Geoffrey J M Parker, Claudia A M Gandini Wheeler-Kingshott, Yipeng Hu

The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module.

Anomaly Detection Image Quality Assessment

Multimodality Biomedical Image Registration using Free Point Transformer Networks

no code implementations5 Aug 2020 Zachary M. C. Baum, Yipeng Hu, Dean C. Barratt

We describe a point-set registration algorithm based on a novel free point transformer (FPT) network, designed for points extracted from multimodal biomedical images for registration tasks, such as those frequently encountered in ultrasound-guided interventional procedures.

Image Registration

Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes

no code implementations9 Jul 2020 Shaheer U. Saeed, Zeike A. Taylor, Mark A. Pinnock, Mark Emberton, Dean C. Barratt, Yipeng Hu

Based on 160, 000 nonlinear FE simulations on clinical imaging data from 320 patients, we demonstrate that the trained networks generalise to unstructured point sets sampled directly from holdout patient segmentation, yielding a near real-time inference and an expected error of 0. 017 mm in predicted nodal displacement.

The challenges of deploying artificial intelligence models in a rapidly evolving pandemic

no code implementations19 May 2020 Yipeng Hu, Joseph Jacob, Geoffrey JM Parker, David J. Hawkes, John R. Hurst, Danail Stoyanov

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks.

COVID-19 Diagnosis Drug Discovery

More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation

no code implementations20 Aug 2019 Yunguan Fu, Maria R. Robu, Bongjin Koo, Crispin Schneider, Stijn van Laarhoven, Danail Stoyanov, Brian Davidson, Matthew J. Clarkson, Yipeng Hu

Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications.

Semantic Segmentation

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 Semantic Segmentation

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

no code implementations27 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

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network

no code implementations18 Dec 2017 Ester Bonmati, Yipeng Hu, Nikhil Sindhwani, Hans Peter Dietz, Jan D'hooge, Dean Barratt, Jan Deprest, Tom Vercauteren

Results show a median Dice similarity coefficient of 0. 90 with an interquartile range of 0. 08, with equivalent performance to the three operators (with a Williams' index of 1. 03), and outperforming a U-Net architecture without the need for batch normalisation.

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

no code implementations5 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

NiftyNet: a deep-learning platform for medical imaging

10 code implementations11 Sep 2017 Eli Gibson, Wenqi Li, Carole Sudre, Lucas Fidon, Dzhoshkun I. Shakir, Guotai Wang, Zach Eaton-Rosen, Robert Gray, Tom Doel, Yipeng Hu, Tom Whyntie, Parashkev Nachev, Marc Modat, Dean C. Barratt, Sébastien Ourselin, M. Jorge Cardoso, Tom Vercauteren

NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.

Data Augmentation Image Generation +2

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.

Image Registration Medical Procedure

Cannot find the paper you are looking for? You can Submit a new open access paper.