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5 papers · 2 filters
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
Christopher Bowles, Liang Chen, Ricardo Guerrero +7
One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not onl…
MRI Super-Resolution using Multi-Channel Total Variation
Mikael Brudfors, Yael Balbastre, Parashkev Nachev +1
This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast. The model recasts the recove…
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning
Mikael Agn, Per Munck af Rosenschöld, Oula Puonti +7
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method comb…
An Algorithm for Learning Shape and Appearance Models without Annotations
John Ashburner, Mikael Brudfors, Kevin Bronik +1
This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. It is based on the idea that having a more accurate s…
Diffeomorphic brain shape modelling using Gauss-Newton optimisation
Yaël Balbastre, Mikael Brudfors, Kevin Bronik +1
Shape modelling describes methods aimed at capturing the natural variability of shapes and commonly relies on probabilistic interpretations of dimensionality reduction techniques s…