Efficient sparse-view medical image classification for low radiation and rapid COVID-19 diagnosis.

Seunghyun Gwak, Sooyoung Yang, Heawon Jeong, Junhu Park, Myungjoo Kang

Abstract read

In one paragraph

Article in Biomedical engineering letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Seunghyun Gwak *Computational Science & Technology, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea.

Sooyoung Yang *Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea.

Heawon JeongComputational Science & Technology, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea.

Junhu ParkComputational Science & Technology, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea.

Myungjoo KangComputational Science & Technology, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study proposes a deep learning-based diagnostic model called the Projection-wise Masked Autoencoder (ProMAE) for rapid and accurate COVID-19 diagnosis using sparse-view CT images. ProMAE employs a column-wise masking strategy during pre-training to effectively learn critical diagnostic features from sinograms, even under extremely sparse conditions. The trained ProMAE can directly classify sparse-view sinograms without requiring CT image reconstruction. Experiments on sparse-view data with 50%, 75%, 85%, 95%, and 99% sparsity show that ProMAE achieves a diagnostic accuracy of over 95% at all sparsity levels and, in particular, outperforms ResNet, ConvNeXt, and conventional MAE models in COVID-19 diagnosis in environments with 85% or higher sparsity. This capability is especially advantageous for the development of portable and flexible imaging systems during large-scale outbreaks such as COVID-19, as it ensures accurate diagnosis while minimizing radiation exposure, making it a vital tool in resource-limited and high-demand settings.

Indexed as

Computed tomographyCOVID-19 diagnosisImage classificiationMasked autoencoderSparse-view sinogram

Identifiers

PMID40621608

DOI10.1007/s13534-025-00478-4

PMCPMC12229398

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.