Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders

2 code implementations • 23 Jun 2020 • Nina Shvetsova, Bart Bakker, Irina Fedulova, Heinrich Schulz, Dmitry V. Dylov

To address this problem, we introduce a new powerful method of image anomaly detection.

Unsupervised Anomaly Detection

Deep learning assessment of breast terminal duct lobular unit involution: towards automated prediction of breast cancer risk

no code implementations • 31 Oct 2019 • Suzanne C. Wetstein, Allison M Onken, Christina Luffman, Gabrielle M Baker, Michael E Pyle, Kevin H Kensler, Ying Liu, Bart Bakker, Ruud Vlutters, Marinus B van Leeuwen, Laura C Collins, Stuart J Schnitt, Josien PW Pluim, Rulla M. Tamimi, Yujing J. Heng, Mitko Veta

A first set of 92 WSIs was annotated for TDLUs, acini and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue.

whole slide images

i-RIM applied to the fastMRI challenge

1 code implementation • 20 Oct 2019 • Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen, Nikita Miriakov, Bart Bakker, Matthan Caan, Max Welling

We, team AImsterdam, summarize our submission to the fastMRI challenge (Zbontar et al., 2018).

Perceptual Image Anomaly Detection

2 code implementations • 12 Sep 2019 • Nina Tuluptceva, Bart Bakker, Irina Fedulova, Anton Konushin

We present a novel method for image anomaly detection, where algorithms that use samples drawn from some distribution of "normal" data, aim to detect out-of-distribution (abnormal) samples.

Anomaly Detection Image Reconstruction