Sources of performance variability in deep learning-based polyp
detection

Sources of performance variability in deep learning-based polyp detection

17 November 2022

Amine Yamlahi

E. Christodoulou

Patrick Godau

Tillmann Persicke

Jörg Gerhard Albert

Lena Maier-Hein

Papers citing "Sources of performance variability in deep learning-based polyp detection"

7 / 7 papers shown

Title
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors Chien-Yao Wang Alexey Bochkovskiy H. Liao ObjD 118 6,383 0 06 Jul 2022
Metrics reloaded: Recommendations for image analysis validation Lena Maier-Hein Annika Reinke Patrick Godau M. Tizabi Florian Buettner ... Aleksei Tiulpin Sotirios A. Tsaftaris Ben Van Calster Gaël Varoquaux Paul F. Jäger 75 224 0 03 Jun 2022
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge Sharib Ali N. Ghatwary Debesh Jha Ece Isik Polat Gorkem Polat ... Dominique Lamarque R. Cannizzaro S. Realdon Thomas de Lange J. East 69 63 0 24 Feb 2022
Common Limitations of Image Processing Metrics: A Picture Story Annika Reinke M. Tizabi Carole H. Sudre Matthias Eisenmann Tim Radsch ... Gaël Varoquaux Manuel Wiesenfarth Ziv R. Yaniv Paul Jäger Lena Maier-Hein 48 144 0 12 Apr 2021
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient Florian Kofler Ivan Ezhov Fabian Isensee F. Balsiger Christoph Berger ... C. Zimmer D. Ankerst Jan Kirschke Benedikt Wiestler Bjoern Menze 46 51 0 10 Mar 2021
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression Zhaohui Zheng Ping Wang Wei Liu Jinze Li Rongguang Ye Dongwei Ren NoLa 88 3,651 0 19 Nov 2019
Microsoft COCO: Common Objects in Context Nayeon Lee Michael Maire Serge J. Belongie Lubomir Bourdev Ross B. Girshick James Hays Pietro Perona Deva Ramanan C. L. Zitnick Piotr Dollár ObjD 295 43,290 0 01 May 2014