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© Research
Publication : 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

A Min-Max Based Hyperparameter Estimation For Domain-Adapted Segmentation Of Amoeboid Cells

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) - 13 Apr 2021

R Sarkar, S Mukherjee, E Labruyère, JC Olivo-Marin

Link to DOI – 10.1109/ISBI48211.2021.9433864

Domain adaption is a tool to fit models trained on a source dataset to characteristically different target samples. During training, such methods typically seek to minimize the segmentation loss, in conjunction with a penalty function for domain misalignment. State-of-the-art solutions are sensitive to heuristically chosen hyper-parameters that dictate the proportion of the two cost functions. We address this issue by introducing a novel strategy for hyper-parameter estimation via min-max optimization of the deep neural network’s loss function. Our solution provides an analytical expression for the model hyper-parameters, which are iteratively estimated during training. Experimental evaluation on both synthetic data and microscopy images of ameoboid cells Entamoeba histolytica attest to the effectiveness of our solution for deep domain-adapted segmentation in bioimaging.

https://ieeexplore.ieee.org/document/9433864