3 citations · 6 across the 6 of their papers we have counts for
6 papers
Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound
Joshua Omolegan, Pak Hei Yeung, Madeleine K. Wyburd +5
Monitoring the growth of subcortical regions of the fetal brain in ultrasound (US) images can help identify the presence of abnormal development. Manually segmenting these regions…
Prototype Learning for Explainable Brain Age Prediction
Linde S. Hesse, Nicola K. Dinsdale, Ana I. L. Namburete
The lack of explainability of deep learning models limits the adoption of such models in clinical practice. Prototype-based models can provide inherent explainable predictions, but…
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical Examples
Linde S. Hesse, Ana I. L. Namburete
Convolutional neural networks (CNNs) have shown exceptional performance for a range of medical imaging tasks. However, conventional CNNs are not able to explain their reasoning pro…
ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation
Pak-Hei Yeung, Linde Hesse, Moska Aliasi +4
The objective of this work is to achieve sensorless reconstruction of a 3D volume from a set of 2D freehand ultrasound images with deep implicit representation. In contrast to the…
Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
Linde S. Hesse, Pim A. de Jong, Josien P. W. Pluim +1
Early detection of lung cancer has been proven to decrease mortality significantly. A recent development in computed tomography (CT), spectral CT, can potentially improve diagnosti…
Intensity augmentation for domain transfer of whole breast segmentation in MRI
Linde S. Hesse, Grey Kuling, Mitko Veta +1
The segmentation of the breast from the chest wall is an important first step in the analysis of breast magnetic resonance images. 3D U-nets have been shown to obtain high segmenta…