Axel Bessy, Thomas Barba, Alexandre Meyer, Hamid Ladjal, Antoine Richard, Mathieu Lefort, Simon Achard
PFIA 2025, June 2025
Abstract
Bibtex
@inproceedings{bessy2025,
TITLE = {{Leveraging Latent Space and Radiomics for Multi-Label Classification of Chest CT Scans}},
AUTHOR = {Bessy, Axel and Barba, Thomas and Meyer, Alexandre and Ladjal, Hamid and Richard, Antoine and Lefort, Mathieu and Achard, Simon},
BOOKTITLE = {PFIA 2025},
ADDRESS = {Dijon, France},
YEAR = {2025},
MONTH = {June},
KEYWORDS = {Multi-label classification ; Computed Tomography ; Transfer learning ; Radiomics ; Medical imaging},
abstract = {In this article, we propose two approaches for multi-label
diagnosis from chest CT images. The first, called Seg2Clf,
leverages the latent space of the encoder from a pre-trained
segmentation model to extract discriminative representations
for classification. The second approach relies on the
extraction and integration of clinical radiomic features, allowing
prediction refinement using a k-NN classifier. These
two complementary methods are part of a broader strategy
aimed at developing automated systems capable of simultaneously
identifying multiple thoracic pathologies with
improved accuracy.}
}