1.3k citations · 1.3k across the 7 of their papers we have counts for
7 papers
Effect of latent space distribution on the segmentation of images with multiple annotations
Ishaan Bhat, Josien P. W. Pluim, Max A. Viergever +1
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution t…
Generalized Probabilistic U-Net for medical image segementation
Ishaan Bhat, Josien P. W. Pluim, Hugo J. Kuijf
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution t…
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection
Ishaan Bhat, Josien P. W. Pluim, Max A. Viergever +1
Deep learning techniques show success in detecting objects in medical images, but still suffer from false-positive predictions that may hinder accurate diagnosis. The estimated unc…
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
Bas H. M. van der Velden, Hugo J. Kuijf, Kenneth G. A. Gilhuijs +1
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis.…
Using uncertainty estimation to reduce false positives in liver lesion detection
Ishaan Bhat, Hugo J. Kuijf, Veronika Cheplygina +1
Despite the successes of deep learning techniques at detecting objects in medical images, false positive detections occur which may hinder an accurate diagnosis. We propose a techn…
Patient-specific fine-tuning of CNNs for follow-up lesion quantification
Mariëlle J. A. Jansen, Hugo J. Kuijf, Ashis K. Dhara +4
Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly more than one imaging examination is available for a patient, but the…
Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation
Mariëlle J. A. Jansen, Hugo J. Kuijf, Josien P. W. Pluim
Most MRI liver segmentation methods use a structural 3D scan as input, such as a T1 or T2 weighted scan. Segmentation performance may be improved by utilizing both structural and f…