1.3k citations · 1.4k across the 4 of their papers we have counts for
6 papers
Optimizing PatchCore for Few/many-shot Anomaly Detection
João Santos, Triet Tran, Oliver Rippel
Few-shot anomaly detection (AD) is an emerging sub-field of general AD, and tries to distinguish between normal and anomalous data using only few selected samples. While newly prop…
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations
Oliver Rippel, Arnav Chavan, Chucai Lei +1
Current state-of-the-art anomaly detection (AD) methods exploit the powerful representations yielded by large-scale ImageNet training. However, catastrophic forgetting prevents the…
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…
AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018
Oliver Rippel, Leon Weninger, Dorit Merhof
Fueled by recent advances in machine learning, there has been tremendous progress in the field of semantic segmentation for the medical image computing community. However, develope…
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection
Oliver Rippel, Patrick Mertens, Dorit Merhof
Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to identifying images and image substructures that deviate significantly from the norm. Popular…
Image-based Survival Analysis for Lung Cancer Patients using CNNs
Christoph Haarburger, Philippe Weitz, Oliver Rippel +1
Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features. With the advent of increasingly large image d…