114 citations · 189 across the 7 of their papers we have counts for
14 papers · 1 filter
Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation
Dwarikanath Mahapatra
In supervised learning for medical image analysis, sample selection methodologies are fundamental to attain optimum system performance promptly and with minimal expert interactions…
Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
Fabian Balsiger, Yannick Soom, Olivier Scheidegger +1
Volumetric image segmentation with convolutional neural networks (CNNs) encounters several challenges, which are specific to medical images. Among these challenges are large volume…
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Behzad Bozorgtabar, Dwarikanath Mahapatra, Hendrik von Teng +4
Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem…
Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
Richard McKinley, Rik Wepfer, Fabian Aschwanden +10
Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper w…
Cascaded V-Net using ROI masks for brain tumor segmentation
Adrià Casamitjana, Marcel Catà, Irina Sánchez +2
In this work we approach the brain tumor segmentation problem with a cascade of two CNNs inspired in the V-Net architecture \cite{VNet}, reformulating residual connections and maki…
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Yannick Suter, Alain Jungo, Michael Rebsamen +4
Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. I…