A Study on Reducing Big Data Image Annotation Burden Through Iterative Expert-In-The-Loop Strategy

作者：Evanjelin Mahmoodi, Zhiyun Xue, Sivaramakrishnan Rajaraman, Sameer Antani · 年份：2023 · DOI：10.1109/bibm58861.2023.10385356 · 被引用次数：3 · 研究领域：COVID-19 diagnosis using AI、Advanced Neural Network Applications、Medical Image Segmentation Techniques

A key challenge in development of reliable and robust medical imaging machine learning solution is the lack of annotated data. This problem becomes particularly significant when big data sets are used. These pose a burden on the annotators to manually segment regions of interest which is a labor intensive and tedious approach. One solution toward addressing this challenge is to use an iterative expert-in-the-loop approach where models that are initially, albeit weakly, trained on a small expert segmented data set are progressively used to expand the training data. In this work, we explore the viability of this approach through two segmentation experiments. The first is a challenging problem of segmenting the buccal mucosa region from photographs of the mouth for subsequent detection and classification of lesions aimed at an oral cancer prediction application. The other is to segment the lung region in chest X-ray (CXR) images. For simplicity and to focus on discovering viability and any associated shortcomings, we limited our scope to just using an off-the-shelf U-Net algorithm to determine if this approach to training data expansion improved segmentation results. Our findings show that for the buccal mucosa segmentation in oral photographs, the method achieved up to 10% improvement in Dice Similarity Coefficient over three iterations on a blinded manually segmented hold-out test set before the performance plateaued. However, the training data set size almost doubled in size ...