4,049 research outputs found
Content-based Image Retrieval Using Constrained Independent Component Analysis: Facial Image Retrieval Based on Compound Queries
Improved Chest Anomaly Localization without Pixel-level Annotation via Image Translation Network Application in Pseudo-paired Registration Domain
Image translation based on a generative adversarial network (GAN-IT) is a
promising method for the precise localization of abnormal regions in chest
X-ray images (AL-CXR) even without pixel-level annotation. However,
heterogeneous unpaired datasets undermine existing methods to extract key
features and distinguish normal from abnormal cases, resulting in inaccurate
and unstable AL-CXR. To address this problem, we propose an improved two-stage
GAN-IT involving registration and data augmentation. For the first stage, we
introduce an advanced deep-learning-based registration technique that virtually
and reasonably converts unpaired data into paired data for learning
registration maps, by sequentially utilizing linear-based global and uniform
coordinate transformation and AI-based non-linear coordinate fine-tuning. This
approach enables the independent and complex coordinate transformation of each
detailed location of the lung while recognizing the entire lung structure,
thereby achieving higher registration performance with resolving inherent
artifacts caused by unpaired conditions. For the second stage, we apply data
augmentation to diversify anomaly locations by swapping the left and right lung
regions on the uniform registered frames, further improving the performance by
alleviating imbalance in data distribution showing left and right lung lesions.
The proposed method is model agnostic and shows consistent AL-CXR performance
improvement in representative AI models. Therefore, we believe GAN-IT for
AL-CXR can be clinically implemented by using our basis framework, even if
learning data are scarce or difficult for the pixel-level disease annotation
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