On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging

4 September 2023

Konstantinos Kamnitsas

Papers citing "On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging"

21 / 21 papers shown

Title
Mahalanobis++: Improving OOD Detection via Feature Normalization Maximilian Mueller Matthias Hein OODD 114 0 0 23 May 2025
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors Peter Lorenz Mario Fernandez Jens Müller Ullrich Kothe AAML 146 1 0 21 Jun 2024
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation Jiamin Liang Yuhao Huang Haoming Li Shuangchi He Xindi Hu Zejian Chen Isabel Kaltenborn Dong Ni OOD 86 45 0 05 Aug 2022
Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild Xuefeng Du Xin Eric Wang Gabriel Gozum Yixuan Li OODD 98 92 0 08 Mar 2022
Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis Anisie Uwimana Ransalu Senanayake OOD MedIm 52 21 0 10 Jul 2021
Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis Christoph Berger Magdalini Paschali Ben Glocker Konstantinos Kamnitsas OOD 93 45 0 06 Jul 2021
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection Jie Jessie Ren Stanislav Fort J. Liu Abhijit Guha Roy Shreyas Padhy Balaji Lakshminarayanan UQCV 173 225 0 16 Jun 2021
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions Abhijit Guha Roy Jie Jessie Ren Shekoofeh Azizi Aaron Loh Vivek Natarajan ... Yun-Hui Liu taylan. cemgil Alan Karthikesalingam Balaji Lakshminarayanan Jim Winkens 113 107 0 08 Apr 2021
Detecting Outliers with Foreign Patch Interpolation Jeremy Tan Benjamin Hou James Batten Huaqi Qiu Bernhard Kainz MedIm 54 49 0 09 Nov 2020
A Unifying Review of Deep and Shallow Anomaly Detection Lukas Ruff Jacob R. Kauffmann Robert A. Vandermeulen G. Montavon Wojciech Samek Marius Kloft Thomas G. Dietterich Klaus-Robert Muller UQCV 117 800 0 24 Sep 2020
The challenges of deploying artificial intelligence models in a rapidly evolving pandemic Yipeng Hu J. Jacob Geoffrey J. M. Parker D. Hawkes J. Hurst Danail Stoyanov OOD 40 65 0 19 May 2020
Why is the Mahalanobis Distance Effective for Anomaly Detection? Ryo Kamoi Kei Kobayashi OODD 172 58 0 01 Mar 2020
FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis E. Çallı K. Murphy Ecem Sogancioglu Bram van Ginneken MedIm OOD 52 15 0 02 Jul 2019
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison Jeremy Irvin Pranav Rajpurkar M. Ko Yifan Yu Silviana Ciurea-Ilcus ... D. Larson C. Langlotz Bhavik Patel M. Lungren A. Ng 112 2,602 0 21 Jan 2019
Deep Anomaly Detection with Outlier Exposure Dan Hendrycks Mantas Mazeika Thomas G. Dietterich OODD 183 1,483 0 11 Dec 2018
Unsupervised domain adaptation for medical imaging segmentation with self-ensembling C. Perone P. Ballester Rodrigo C. Barros Julien Cohen-Adad OOD 87 208 0 14 Nov 2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks Kimin Lee Kibok Lee Honglak Lee Jinwoo Shin OODD 187 2,060 0 10 Jul 2018
Confounding variables can degrade generalization performance of radiological deep learning models J. Zech Marcus A. Badgeley Manway Liu A. Costa J. Titano Eric K. Oermann OOD 85 1,179 0 02 Jul 2018
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks Dan Hendrycks Kevin Gimpel UQCV 166 3,468 0 07 Oct 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 831 9,345 0 06 Jun 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 280 19,107 0 20 Dec 2014