1.3k citations · 1.6k across the 14 of their papers we have counts for
14 papers
Uncertainty-Aware Semi-Supervised Learning for Prostate MRI Zonal Segmentation
Matin Hosseinzadeh, Anindo Saha, Joeran Bosma +1
Quality of deep convolutional neural network predictions strongly depends on the size of the training dataset and the quality of the annotations. Creating annotations, especially f…
Medical diffusion on a budget: Textual Inversion for medical image generation
Bram de Wilde, Anindo Saha, Maarten de Rooij +2
Diffusion models for text-to-image generation, known for their efficiency, accessibility, and quality, have gained popularity. While inference with these systems on consumer-grade…
Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
Yiwen Li, Yunguan Fu, Qianye Yang +6
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class a…
Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography
Natália Alves, Megan Schuurmans, Geke Litjens +3
Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC) but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomogra…
Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI
Joeran S. Bosma, Anindo Saha, Matin Hosseinzadeh +3
Deep learning-based diagnostic performance increases with more annotated data, but large-scale manual annotations are expensive and labour-intensive. Experts evaluate diagnostic im…
Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI Should Different Clinical Objectives Mandate Different Loss Functions?
Anindo Saha, Joeran Bosma, Jasper Linmans +2
We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets, re…
Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning
Bram de Wilde, Richard P. G. ten Broek, Henkjan Huisman
Adhesions are an important cause of chronic pain following abdominal surgery. Recent developments in abdominal cine-MRI have enabled the non-invasive diagnosis of adhesions. Adhesi…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction
Anindo Saha, Matin Hosseinzadeh, Henkjan Huisman
We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imag…
Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI
Anindo Saha, Matin Hosseinzadeh, Henkjan Huisman
We hypothesize that anatomical priors can be viable mediums to infuse domain-specific clinical knowledge into state-of-the-art convolutional neural networks (CNN) based on the U-Ne…
Effect of Adding Probabilistic Zonal Prior in Deep Learning-based Prostate Cancer Detection
Matin Hosseinzadeh, Patrick Brand, Henkjan Huisman
We propose and evaluate a novel method for automatically detecting clinically significant prostate cancer (csPCa) in bi-parametric magnetic resonance imaging (bpMRI). Prostate zone…
Supervised Uncertainty Quantification for Segmentation with Multiple Annotations
Shi Hu, Daniel Worrall, Stefan Knegt +3
The accurate estimation of predictive uncertainty carries importance in medical scenarios such as lung node segmentation. Unfortunately, most existing works on predictive uncertain…
Autoencoders for Multi-Label Prostate MR Segmentation
Ard de Gelder, Henkjan Huisman
Organ image segmentation can be improved by implementing prior knowledge about the anatomy. One way of doing this is by training an autoencoder to learn a lowdimensional representa…
Automatic segmentation of prostate zones
Germonda Mooij, Ines Bagulho, Henkjan Huisman
Convolutional networks have become state-of-the-art techniques for automatic medical image analysis, with the U-net architecture being the most popular at this moment. In this arti…