Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook

Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook

8 December 2023

Michael Huttemann

Sanaz Karimijafarbigloo

Anke Schmeink

Papers citing "Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook"

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Title
Transport-Related Surface Detection with Machine Learning: Analyzing Temporal Trends in Madrid and Vienna Miguel Ureña Pliego Rubén Martínez Marín Nianfang Shi Takeru Shibayama Ulrich Leth Miguel Marchamalo Sacristán 53 0 0 19 Mar 2025
GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI Data Cecilia Diana-Albelda Roberto Alcover-Couso Álvaro García-Martín Jesús Bescós Marcos Escudero-Viñolo 40 1 0 06 Mar 2025
Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision Bobby Azad Reza Azad Sania Eskandari Afshin Bozorgpour A. Kazerouni I. Rekik Dorit Merhof VLM MedIm 93 59 0 28 Oct 2023
Semi-supervised Multi-task Learning for Semantics and Depth Yufeng Wang Yi-Hsuan Tsai Wei-Chih Hung Wenrui Ding Shuo Liu Ming-Hsuan Yang 29 18 0 14 Oct 2021
MISSFormer: An Effective Medical Image Segmentation Transformer Xiaohong Huang Zhifang Deng Dandan Li Xueguang Yuan ViT MedIm 87 172 0 15 Sep 2021
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation Michael Yeung Evis Sala Carola-Bibiane Schönlieb L. Rundo 25 393 0 08 Feb 2021
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 232 75,445 0 18 May 2015