Search Results for author: Josien P. W. Pluim

Found 30 papers, 11 papers with code

Histogram- and Diffusion-Based Medical Out-of-Distribution Detection

no code implementations12 Oct 2023 Evi M. C. Huijben, Sina Amirrajab, Josien P. W. Pluim

Out-of-distribution (OOD) detection is crucial for the safety and reliability of artificial intelligence algorithms, especially in the medical domain.

Out-of-Distribution Detection Out of Distribution (OOD) Detection +1

Effect of latent space distribution on the segmentation of images with multiple annotations

1 code implementation26 Apr 2023 Ishaan Bhat, Josien P. W. Pluim, Max A. Viergever, 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 that can better approximate the uncertainty in the reference segmentations.

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

no code implementations16 Jan 2023 Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni, Zhang Li, Tao Tan, Abhir Bhalerao, Jiabo Ma, Jiamei Sun, Johnathan Pocock, Josien P. W. Pluim, Navid Alemi Koohbanani, Raja Muhammad Saad Bashir, Shan E Ahmed Raza, Sibo Liu, Simon Graham, Suzanne Wetstein, Syed Ali Khurram, Thomas Watson, Nasir Rajpoot, Mitko Veta, Francesco Ciompi

Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.

Generalized Probabilistic U-Net for medical image segementation

1 code implementation26 Jul 2022 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 that can better approximate the uncertainty in the reference segmentations.

Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection

1 code implementation22 Jun 2022 Ishaan Bhat, Josien P. W. Pluim, Max A. Viergever, Hugo J. Kuijf

We study the role played by features computed from neural network uncertainty estimates and shape-based features computed from binary predictions in reducing false positives in liver lesion detection by developing a classification-based post-processing step for different uncertainty estimation methods.

Lesion Detection

ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification

1 code implementation27 Jul 2021 Ralf Raumanns, Gerard Schouten, Max Joosten, Josien P. W. Pluim, Veronika Cheplygina

In this paper we first analyse the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources.

Lesion Classification Multi-Task Learning +1

Corneal Pachymetry by AS-OCT after Descemet's Membrane Endothelial Keratoplasty

no code implementations15 Feb 2021 Friso G. Heslinga, Ruben T. Lucassen, Myrthe A. van den Berg, Luuk van der Hoek, Josien P. W. Pluim, Javier Cabrerizo, Mark Alberti, Mitko Veta

In this research, deep learning is used to automatically delineate the corneal interfaces and measure corneal thickness with high accuracy in post-DMEK AS-OCT B-scans.

Using uncertainty estimation to reduce false positives in liver lesion detection

no code implementations12 Jan 2021 Ishaan Bhat, Hugo J. Kuijf, Veronika Cheplygina, Josien P. W. Pluim

We find that the use of a dropout rate of 0. 5 produces the least number of false positives in the neural network predictions and the trained classifier filters out approximately 90% of these false positives detections in the test-set.

Lesion Detection

Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology

no code implementations26 Aug 2020 Maxime W. Lafarge, Josien P. W. Pluim, Mitko Veta

However, some generative factors that cause irrelevant variations in images can potentially get entangled in such a learned representation causing the risk of negatively affecting any subsequent use.

Representation Learning

Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis

1 code implementation20 Feb 2020 Maxime W. Lafarge, Erik J. Bekkers, Josien P. W. Pluim, Remco Duits, Mitko Veta

This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.

BIG-bench Machine Learning Breast Tumour Classification +6

Liver segmentation and metastases detection in MR images using convolutional neural networks

1 code implementation15 Oct 2019 Mariëlle J. A. Jansen, Hugo J. Kuijf, Maarten Niekel, Wouter B. Veldhuis, Frank J. Wessels, Max A. Viergever, Josien P. W. Pluim

Primary tumors have a high likelihood of developing metastases in the liver and early detection of these metastases is crucial for patient outcome.

Liver Segmentation

Motion correction of dynamic contrast enhanced MRI of the liver

no code implementations22 Aug 2019 Mariëlle J. A. Jansen, Wouter B. Veldhuis, Maarten S. van Leeuwen, Josien P. W. Pluim

Compared to a pairwise method or no registration, groupwise registration provided better alignment.

Crowd disagreement about medical images is informative

no code implementations21 Jun 2018 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.

Inferring a Third Spatial Dimension from 2D Histological Images

no code implementations10 Jan 2018 Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta

Histological images are obtained by transmitting light through a tissue specimen that has been stained in order to produce contrast.

Data Augmentation

Isointense infant brain MRI segmentation with a dilated convolutional neural network

no code implementations9 Aug 2017 Pim Moeskops, Josien P. W. Pluim

Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter.

Infant Brain Mri Segmentation MRI segmentation +1

Adversarial training and dilated convolutions for brain MRI segmentation

no code implementations11 Jul 2017 Pim Moeskops, Mitko Veta, Maxime W. Lafarge, Koen A. J. Eppenhof, Josien P. W. Pluim

To this end, we include an additional loss function that motivates the network to generate segmentations that are difficult to distinguish from manual segmentations.

Image Segmentation MRI segmentation +2

Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation

no code implementations20 Jun 2016 Mitko Veta, Paul J. van Diest, Josien P. W. Pluim

We hypothesize that given an image of a tumor region with known nuclei locations, the area of the individual nuclei and region statistics such as the MNA can be reliably computed directly from the image data by employing a machine learning model, without the intermediate step of nuclei segmentation.

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