Search Results for author: Yiwen Li

Found 10 papers, 6 papers with code

'One size doesn't fit all': Learning how many Examples to use for In-Context Learning for Improved Text Classification

no code implementations11 Mar 2024 Manish Chandra, Debasis Ganguly, Yiwen Li, Iadh Ounis

While existing work uses a static number of examples during inference for each data instance, in this paper we propose a novel methodology of dynamically adapting the number of examples as per the data.

In-Context Learning text-classification +1

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

Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation

1 code implementation10 Mar 2023 Yunguan Fu, Yiwen Li, Shaheer U. Saeed, Matthew J. Clarkson, Yipeng Hu

Recently, denoising diffusion probabilistic models (DDPM) have been applied to image segmentation by generating segmentation masks conditioned on images, while the applications were mainly limited to 2D networks without exploiting potential benefits from the 3D formulation.

Denoising Image Segmentation +2

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

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

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 Segmentation +1

GroSS: Group-Size Series Decomposition for Grouped Architecture Search

1 code implementation ECCV 2020 Henry Howard-Jenkins, Yiwen Li, Victor A. Prisacariu

We present a novel approach which is able to explore the configuration of grouped convolutions within neural networks.

GroSS Decomposition: Group-Size Series Decomposition for Whole Search-Space Training

no code implementations25 Sep 2019 Henry Howard-Jenkins, Yiwen Li, Victor Adrian Prisacariu

We present Group-size Series (GroSS) decomposition, a mathematical formulation of tensor factorisation into a series of approximations of increasing rank terms.

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