Search Results for author: Henkjan Huisman

Found 11 papers, 5 papers with code

Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning

no code implementations17 Jan 2022 Yiwen Li, Yunguan Fu, Qianye Yang, Zhe Min, Wen Yan, Henkjan Huisman, Dean Barratt, Victor Adrian Prisacariu, Yipeng Hu

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 are available from local healthcare providers, is sought-after.

Medical Image Segmentation Semantic Segmentation

Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI

1 code implementation9 Dec 2021 Joeran S. Bosma, Anindo Saha, Matin Hosseinzadeh, Ilse Slootweg, Maarten de Rooij, Henkjan Huisman

Semi-supervised training was 14$\times$ more annotation-efficient for case-based performance and 6$\times$ more annotation-efficient for lesion-based performance.

Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

no code implementations30 Nov 2021 Natália Alves, Megan Schuurmans, Geke Litjens, Joeran S. Bosma, John Hermans, Henkjan Huisman

In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions.

Lesion Detection

Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI $-$Should Different Clinical Objectives Mandate Different Loss Functions?

1 code implementation25 Oct 2021 Anindo Saha, Joeran Bosma, Jasper Linmans, Matin Hosseinzadeh, Henkjan Huisman

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, require different loss functions for optimal performance due to inherent differences in their clinical objectives.

Lesion Detection Panoptic Segmentation

Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning

no code implementations15 Jun 2021 Bram de Wilde, Richard P. G. ten Broek, Henkjan Huisman

We experimented with spatio-temporal deep learning architectures centered around a ConvGRU architecture.

Time Series

End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction

1 code implementation8 Jan 2021 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 imaging (bpMRI).

Deep Attention

Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI

1 code implementation31 Oct 2020 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-Net architecture.

Supervised Uncertainty Quantification for Segmentation with Multiple Annotations

no code implementations3 Jul 2019 Shi Hu, Daniel Worrall, Stefan Knegt, Bas Veeling, Henkjan Huisman, Max Welling

The accurate estimation of predictive uncertainty carries importance in medical scenarios such as lung node segmentation.

Automatic segmentation of prostate zones

no code implementations19 Jun 2018 Germonda Mooij, Ines Bagulho, Henkjan Huisman

We show that to segment more tissues the network replaces feature maps that were dedicated to detecting prostate peripheral zones, by feature maps detecting the surrounding tissues.

Autoencoders for Multi-Label Prostate MR Segmentation

no code implementations9 Jun 2018 Ard de Gelder, Henkjan Huisman

Organ image segmentation can be improved by implementing prior knowledge about the anatomy.

Semantic Segmentation

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