© FUJIFILM Corporation

A Training Method to Build Characterization Models for Pulmonary Nodules in CT Images Using Radiology Reports


To reduce the cost of the training data construction, authors [1] propose a pseudo-labeling approach for automatic characterization of pulmonary nodules, i.e., image labels automatically retrieved from radiology reports. In the experiments, the classifier trained using their pseudo-labeling approach achieves almost the same performance as the one trained on ground truth labels on images manually annotated by radiologists. In the future, this method will help to build a CAD system that performs more detailed characterization.

Figure 1. Previous approach [based on [1]] (Expert checks images and annotates labels)
Figure 2. Proposed approach [based on [1]] (pseudo-labels are automatically predicted based on radiology reports)
Figure 3. Performance comparison between proposed method versus manual labeling. [1]
Figure 4. Examples of the model output trained on proposed method. [1]

[1] Momoki, Yohei, et al. “Characterization of Pulmonary Nodules in Computed Tomography Images Based on Pseudo-Labeling Using Radiology Reports.” IEEE Transactions on Circuits and Systems for Video Technology 32.5 (2021): 2582-2591.

DOI: https://doi.org/10.1109/TCSVT.2021.3073021

CAUTION:This is Fujifilm Global Website. Fujifilm makes no representation that products on this website are commercially available in all countries. Approved uses of products vary by country and region. Specifications and appearance of products are subject to change without notice.