SoftSeg: Advantages of soft versus binary training for image segmentation

18 November 2020

Papers citing "SoftSeg: Advantages of soft versus binary training for image segmentation"

8 / 8 papers shown

Title
Confidence-Based Annotation Of Brain Tumours In Ultrasound Alistair Weld L. Dixon Alfie Roddan Giulio Anichini Sophie Camp Stamatia Giannarou 52 0 0 24 Feb 2025
Noisy Image Segmentation With Soft-Dice M. Nordström Henrik Hult A. Maki F. Löfman 26 2 0 03 Apr 2023
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss B. Felfeliyan A. Hareendranathan G. Kuntze S. Wichuk Nils D. Forkert Jacob L. Jaremko J. Ronsky NoLa 21 2 0 16 Sep 2022
Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images Galadrielle Humblot-Renaux Letizia Marchegiani T. Moeslund Rikke Gade SSeg EgoV 20 14 0 15 Sep 2021
Impact of individual rater style on deep learning uncertainty in medical imaging segmentation Olivier Vincent C. Gros Julien Cohen-Adad 17 10 0 05 May 2021
End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction A. Saha M. Hosseinzadeh Henkjan Huisman MedIm 30 129 0 08 Jan 2021
A Survey on Deep Learning in Medical Image Analysis G. Litjens Thijs Kooi B. Bejnordi A. Setio F. Ciompi Mohsen Ghafoorian Jeroen van der Laak Bram van Ginneken C. I. Sánchez OOD 278 10,599 0 19 Feb 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,109 0 06 Jun 2015