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20202026
most citedImage quality assessment for machine learning tasks using meta-reinforcement learning

51 citations · 103 across the 12 of their papers we have counts for

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5 papers · 1 filter

cs.CV2026

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability

Qi Li, Yuliang Huang, Shaheer U. Saeed +7

Deep learning-based medical image segmentation models are trained using annotations that exhibit systematic bias and variability across raters. While probabilistic multi-rater appr…

cs.CV2024

Biomechanics-informed Non-rigid Medical Image Registration and its Inverse Material Property Estimation with Linear and Nonlinear Elasticity

Zhe Min, Zachary M. C. Baum, Shaheer U. Saeed +4

This paper investigates both biomechanical-constrained non-rigid medical image registrations and accurate identifications of material properties for soft tissues, using physics-inf…

cs.CV2023

Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images

Weixi Yi, Vasilis Stavrinides, Zachary M. C. Baum +5

We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision the segmentation as a boundary detection probl…

cs.CV2021

Adaptable image quality assessment using meta-reinforcement learning of task amenability

Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides +8

The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjec…

cs.CV2020

Multimodality Biomedical Image Registration using Free Point Transformer Networks

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 registrati…