Search Results for author:
Found 12 papers, 7 papers with code
Improving Knowledge Distillation via Category Structure
Intra-category structure penalizes the structured relations in samples from the same category and inter-category structure focuses on cross-category relations at a category level.
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas.
Neural Network Diffusion
The autoencoder extracts latent representations of a subset of the trained network parameters.
Expectation Maximization Pseudo Labels
In the remainder of the paper, we showcase the applications of pseudo-labelling and its generalised form, Bayesian Pseudo-Labelling, in the semi-supervised segmentation of medical images.
Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation
Secondly, we propose a semi-supervised medical image segmentation method purely based on the original pseudo labelling, namely SegPL.
Progressive Subsampling for Oversampled Data -- Application to Quantitative MRI
We build upon a recent dual-network approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI measurement sampling-reconstruction challenge, but suffers from deep learning training instability, by subsampling with a hard decision boundary.
VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation
Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease.
MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels
The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations.
Learning to Address Intra-segment Misclassification in Retinal Imaging
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity.
Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set
For the segmented masks of intact lung and infected regions, the best method could achieve 0. 972 and 0. 757 measure in mean Dice similarity coefficient on our test benchmark.
A Refined Equilibrium Generative Adversarial Network for Retinal Vessel Segmentation
In this work, we present an end-to-end synthetic neural network, containing a symmetric equilibrium generative adversarial network (SEGAN), multi-scale features refine blocks (MSFRB), and attention mechanism (AM) to enhance the performance on vessel segmentation.
Dual-attention Focused Module for Weakly Supervised Object Localization
In each branch, the input feature map is deduced into an enhancement map and a mask map, thereby highlighting the most discriminative parts or hiding them.
