M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks

• URL: http://arxiv.org/abs/2306.12376v1
• Date: Wed, 21 Jun 2023 16:40:37 GMT
• Title: M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks
• Authors: Bidur Khanal, Binod Bhattarai, Bishesh Khanal, Danail Stoyanov, Cristian A. Linte
• Abstract summary: Active learning attempts to minimize the need for large annotated samples by actively sampling the most informative examples for annotation. We propose a Multimodal Variational Adversarial Active Learning (M-VAAL) method that uses auxiliary information from additional modalities to enhance the active sampling.
• Score: 16.85572580186212
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation. Active learning attempts to minimize the need for large annotated samples by actively sampling the most informative examples for annotation. These examples contribute significantly to improving the performance of supervised machine learning models, and thus, active learning can play an essential role in selecting the most appropriate information in deep learning-based diagnosis, clinical assessments, and treatment planning. Although some existing works have proposed methods for sampling the best examples for annotation in medical image analysis, they are not task-agnostic and do not use multimodal auxiliary information in the sampler, which has the potential to increase robustness. Therefore, in this work, we propose a Multimodal Variational Adversarial Active Learning (M-VAAL) method that uses auxiliary information from additional modalities to enhance the active sampling. We applied our method to two datasets: i) brain tumor segmentation and multi-label classification using the BraTS2018 dataset, and ii) chest X-ray image classification using the COVID-QU-Ex dataset. Our results show a promising direction toward data-efficient learning under limited annotations.

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