Search Results for author: Wen Yan

Found 13 papers, 5 papers with code

Weakly supervised localisation of prostate cancer using reinforcement learning for bi-parametric MR images

no code implementations21 Feb 2024 Martynas Pocius, Wen Yan, Dean C. Barratt, Mark Emberton, Matthew J. Clarkson, Yipeng Hu, Shaheer U. Saeed

The object-presence classifier may then inform the controller of its localisation quality by quantifying the likelihood of the image containing an object.

Multiple Instance Learning Object

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

Combiner and HyperCombiner Networks: Rules to Combine Multimodality MR Images for Prostate Cancer Localisation

no code implementations17 Jul 2023 Wen Yan, Bernard Chiu, Ziyi Shen, Qianye Yang, Tom Syer, Zhe Min, Shonit Punwani, Mark Emberton, David Atkinson, Dean C. Barratt, Yipeng Hu

One of the distinct characteristics in radiologists' reading of multiparametric prostate MR scans, using reporting systems such as PI-RADS v2. 1, is to score individual types of MR modalities, T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer.

Image Segmentation Semantic Segmentation

Machine Learning Force Fields with Data Cost Aware Training

1 code implementation5 Jun 2023 Alexander Bukharin, Tianyi Liu, Shengjie Wang, Simiao Zuo, Weihao Gao, Wen Yan, Tuo Zhao

To address this issue, we propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.

Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion

no code implementations3 Mar 2023 Shaheer U. Saeed, Tom Syer, Wen Yan, Qianye Yang, Mark Emberton, Shonit Punwani, Matthew J. Clarkson, Dean C. Barratt, Yipeng Hu

For the first time, we evaluate the realism of the generated pathology by blind expert identification of the presence of suspected lesions, where we find that the clinician performs similarly for both real and synthesised images, with a 2. 9 percentage point difference in lesion identification accuracy between real and synthesised images, demonstrating the potentials in radiological training purposes.

Image Generation

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

Cross-Modality Image Registration using a Training-Time Privileged Third Modality

1 code implementation26 Jul 2022 Qianye Yang, David Atkinson, Yunguan Fu, Tom Syer, Wen Yan, Shonit Punwani, Matthew J. Clarkson, Dean C. Barratt, Tom Vercauteren, Yipeng Hu

In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered.

Image Registration

The impact of using voxel-level segmentation metrics on evaluating multifocal prostate cancer localisation

no code implementations30 Mar 2022 Wen Yan, Qianye Yang, Tom Syer, Zhe Min, Shonit Punwani, Mark Emberton, Dean C. Barratt, Bernard Chiu, Yipeng Hu

However, the differences in false-positives and false-negatives, between the actual errors and the perceived counterparts if DSC is used, can be as high as 152 and 154, respectively, out of the 357 test set lesions.

Image Segmentation Medical Image Segmentation +3

Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation

1 code implementation20 Feb 2022 Shaheer U. Saeed, Wen Yan, Yunguan Fu, Francesco Giganti, Qianye Yang, Zachary M. C. Baum, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, Mark Emberton, Dean C. Barratt, Yipeng Hu

This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e. g. segmentation and classification neural networks in modern clinical applications.

Image Quality Assessment

Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning

no code implementations17 Jan 2022 Yiwen Li, Yunguan Fu, Qianye Yang, Zhe Min, Wen Yan, Henkjan Huisman, Dean Barratt, Victor Adrian Prisacariu, Yipeng Hu

The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after.

Anatomy Image Segmentation +3

Unsupervised End-to-end Learning for Deformable Medical Image Registration

no code implementations23 Nov 2017 Siyuan Shan, Wen Yan, Xiaoqing Guo, Eric I-Chao Chang, Yubo Fan, Yan Xu

The contributions of our algorithm are threefold: (1) We transplant traditional image registration algorithms to an end-to-end convolutional neural network framework, while maintaining the unsupervised nature of image registration problems.

Deformable Medical Image Registration Image Registration +1

Flexibly imposing periodicity in kernel independent FMM: A Multipole-To-Local operator approach

2 code implementations4 May 2017 Wen Yan, Michael Shelley

An important but missing component in the application of the kernel independent fast multipole method (KIFMM) is the capability for flexibly and efficiently imposing singly, doubly, and triply periodic boundary conditions.

Numerical Analysis

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