bibtext: @InProceedings{ Kha_DomainAdapt_MICCAI2024,
                   author = { Khan, Misaal and Singh, Richa and Vatsa, Mayank and Singh, Kuldeep },
                   title = { { DomainAdapt: Leveraging Multitask Learning and Domain Insights for Children’s Nutritional Status Assessment } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
abstract: This study presents a novel approach for automating nutritional status assessments in children, designed to assist health workers in public health contexts. We introduce DomainAdapt a novel dynamic task-weighing method within a multitask learning framework, which leverages domain knowledge and Mutual Information to balance task-specific losses, enhancing the learning efficiency for nutritional status screening. We have also assembled an unprecedented dataset comprising 16,938 multipose images and anthropometric data from 2,141 children across various settings, marking a significant first in this domain. Through rigorous testing, this method demonstrates superior performance in identifying malnutrition in children and predicting their anthropometric measures compared to existing multitask learning approaches. Dataset is available at : iab-rubric.org/resources/healthcare-datasets/anthrovision-dataset
title: DomainAdapt: Leveraging Multitask Learning and Domain Insights for Children’s Nutritional Status Assessment
authors: Khan, Misaal Singh, Richa Vatsa, Mayank Singh, Kuldeep