arXiv subjects

Sara Antonijevic

Publications and source records attributed to Sara Antonijevic.

4 recordsLinked to original sources

arXiv2026

Quaternion Nondecimated Wavelet Descriptors for Multiclass Breast Histology Classification

Breast histology images carry diagnostic information in color, texture, orientation, and tissue architecture across a range of scales. In H&E microscopy this information is inherently chromatic and is not fully recovered when the red, green, and blue (RGB) channels are reduced to grayscale or transformed as independent scalar images. We propose an interpretable quaternion nondecimated wavelet framework for breast histology classification. Each RGB image is encoded as a pure quaternion field, and a quaternion nondecimated wavelet transform in two dimensions (QNDWT2D) produces multiscale, directional, color-coupled coefficient fields on the original image grid, keeping color as a single vector quantity rather than three separate channels. From these coefficients we build interpretable feature families summarizing stain balance, wavelet energy, amplitude heterogeneity, quaternion phase concentration, color-axis geometry, directional anisotropy, orientation entropy, and scale-dependent energy decay, each tied to a histopathological property such as nuclear density or glandular organization. We evaluate the descriptors on the BreAst Cancer Histology (BACH) challenge, a balanced four-class set of normal, benign, in situ, and invasive tissue, using a radial-kernel support vector machine (SVM) with repeated nested cross-validation. The descriptors yield balanced recognition across classes, with errors concentrated among adjacent categories while normal and invasive are rarely reversed. Permutation importance shows that directional, phase-concentration, anisotropy, scale, and amplitude-variability groups all contribute, indicating that the classifier draws on genuine quaternion and multiscale geometry rather than global color alone. The framework uses no pretrained networks, learned filters, or external databases, offering a reproducible, interpretable baseline for computational pathology.

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arXiv2026

Masked complex non-decimated wavelet features for patient-level classification of contrast-enhanced mammography

Contrast-enhanced spectral mammography (CESM) acquires two images of each breast, a low-energy image and a recombined contrast image, but two questions central to building a classifier on them remain unsettled: whether the two image types carry comparable malignancy signal, and how a patient's several images should be combined into a single decision. Both are hard to answer reliably, because most published CESM classifiers split cross-validation folds at the image level, letting images of the same patient fall in both training and test sets and inflating reported performance. We pair a masked complex non-decimated wavelet feature bank with an elastic-net logistic classifier, evaluated under repeated patient-grouped nested cross-validation with patient-cluster bootstrap inference on the CDD-CESM dataset (1,880 images, 308 patients); under this leakage-free evaluation the inflation from testing on previously seen patients is negligible. On normal-versus-malignant detection, the two acquisitions are statistically indistinguishable in patient-level AUC under the proposed evaluation framework. Under single-image fusion the contrast image reaches a patient-level AUC of 0.874 (95% CI 0.827-0.918) and the low-energy image is statistically indistinguishable from it, yet the two encode malignancy through disjoint, interpretable channels: phase coherence on the low-energy image and magnitude distribution on the contrast image. The framework matches a pretrained ResNet-50 representation at the patient level, but whereas the frozen deep representation is not directly interpretable at the level of individual predictors, every predictor in the wavelet representation carries an explicit physical meaning. The result is a transparent, leakage-free baseline against which future CESM classifiers can be measured.

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arXiv2026

Bayesian Inference for Incomplete 2x2 Diagnostic Tables

Incomplete reporting of diagnostic accuracy data remains a persistent problem in medical research. In many studies, only part of the 2x2 diagnostic table is reported, leaving denominators for diseased and non-diseased groups unknown and preventing direct calculation of sensitivity, specificity, predictive values, and related operating characteristics. To address this limitation, we develop hierarchical Bayesian models for reconstructing incomplete 2x2 diagnostic tables from such partial information. Two motivating scenarios are considered: one in which only a single test-outcome row is observed, and another in which true positives, false positives, and the total sample size are reported but the remaining cells are missing. The proposed models are illustrated on a benchmark breast MRI study with complete counts, treated as partially observed in order to assess reconstruction performance under controlled missingness. The framework yields posterior inference for the missing cell counts and associated diagnostic measures, together with uncertainty quantification in weakly identified settings.

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arXiv2025

Bayesian Meta-Analysis with Application in Dental Studies

Dental caries remain a persistent global health challenge, and fluoride varnish is widely used as a preventive intervention. This study synthesizes evidence from multiple clinical trials to evaluate the effectiveness of fluoride varnish in reducing Decayed-Missing-Filled (DMF) surfaces. The principal measure of efficacy is the Prevented Fraction (PF), representing the proportional reduction in caries relative to untreated controls. A comprehensive meta-analysis was conducted using fixed-effect and random-effects models, complemented by hierarchical Bayesian inference. The Bayesian framework incorporated multiple prior distributions on between-study variance, including Pareto, half-normal, uniform, beta, and scaled chi-square forms, to assess robustness under alternative heterogeneity assumptions. Across all specifications, the pooled estimate indicated an approximate 43% reduction in caries incidence, with credible intervals consistently excluding the null. Compared to classical methods, the Bayesian approach provided richer uncertainty quantification through full posterior distributions, allowed principled incorporation of prior evidence, and offered improved inference under heterogeneity and small-sample conditions. The stability of posterior estimates across diverse priors reinforces the robustness and reliability of the conclusions. Overall, findings confirm fluoride varnish as an effective and consistent preventive measure, and demonstrate the value of Bayesian hierarchical modeling as a powerful complement to traditional meta-analytic techniques in dental public health research.

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