Public articles linked to the same research event.
Chemical & Biomedical Imaging This study combines label-free optical diffraction tomography (ODT) with an interpretable machine-learning framework to extract physically interpretable morphological and biophysical descriptors (sphericity, solidity, eccentricity, dry mass, maximum refractive index) and self-supervised vision transformer (ViT) image representations from three-dimensional refractive index tomograms of red blood cells from synchronized P. falciparum cultures, finding that gametocyte-stage infected RBCs show significantly reduced sphericity and increased eccentricity, with combined features achieving 88.3% accuracy in multiclass classification (normal, ring, gametocyte) and 98.
This study combines label-free optical diffraction tomography (ODT) with an interpretable machine-learning framework to extract physically interpretable morphological and biophysical descriptors (sphericity, solidity, eccentricity, dry mass, maximum refractive index) and self-supervised vision transformer (ViT) image representations from three-dimensional refractive index tomograms of red blood cells from synchronized P. falciparum cultures, finding that gametocyte-stage infected RBCs show significantly reduced sphericity and increased eccentricity, with combined features achieving 88.3% accuracy in multiclass classification (normal, ring, gametocyte) and 98.
This study combines label-free optical diffraction tomography (ODT) with an interpretable machine-learning framework to extract physically interpretable morphological and biophysical descriptors (sphericity, solidity, eccentricity, dry mass, maximum refractive index) and self-supervised vision transformer (ViT) image representations from three-dimensional refractive index tomograms of red blood cells from synchronized P. falciparum cultures, finding that gametocyte-stage infected RBCs show significantly reduced sphericity and increased eccentricity, with combined features achieving 88.3% accuracy in multiclass classification (normal, ring, gametocyte) and 98.
This study combines label-free optical diffraction tomography (ODT) with an interpretable machine-learning framework to extract physically interpretable morphological and biophysical descriptors (sphericity, solidity, eccentricity, dry mass, maximum refractive index) and self-supervised vision transformer (ViT) image representations from three-dimensional refractive index tomograms of red blood cells from synchronized P. falciparum cultures, finding that gametocyte-stage infected RBCs show significantly reduced sphericity and increased eccentricity, with combined features achieving 88.3% accuracy in multiclass classification (normal, ring, gametocyte) and 98.