31 citations · 95 across the 13 of their papers we have counts for
13 papers · 1 filter
Self-supervised Pretraining for Cardiovascular Magnetic Resonance Cine Segmentation
Rob A. J. de Mooij, Josien P. W. Pluim, Cian M. Scannell
Self-supervised pretraining (SSP) has shown promising results in learning from large unlabeled datasets and, thus, could be useful for automated cardiovascular magnetic resonance (…
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…
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
Maxime W. Lafarge, Erik J. Bekkers, Josien P. W. Pluim +2
Rotation-invariance is a desired property of machine-learning models for medical image analysis and in particular for computational pathology applications. We propose a framework t…
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
Mitko Veta, Yujing J. Heng, Nikolas Stathonikos +30
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is highly subjective an…
Crowd disagreement about medical images is informative
Veronika Cheplygina, Josien P. W. Pluim
Classifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds. However, disagreement between annotato…
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Veronika Cheplygina, Marleen de Bruijne, Josien P. W. Pluim
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised…