Mahdi Yousefi | Searcharxiv

arXiv2025

A Semi-Supervised Inf-Net Framework for CT-Based Lung Nodule Analysis with a Conceptual Extension Toward Genomic Integration

Lung cancer is a primary contributor to cancer-related mortality globally, highlighting the necessity for precise early detection of pulmonary nodules through low-dose CT (LDCT) imaging. Deep learning methods have improved nodule detection and classification; however, their performance is frequently limited by the availability of annotated data and variability among imaging centers. This research presents a CT-driven, semi-supervised framework utilizing the Inf-Net architecture to enhance lung nodule analysis with minimal annotation. The model incorporates multi-scale feature aggregation, Reverse Attention refinement, and pseudo-labeling to efficiently utilize unlabeled CT slices. Experiments conducted on subsets of the LUNA16 dataset indicate that the supervised Inf-Net attains a score of 0.825 on 10,000 labeled slices. In contrast, the semi-supervised variant achieves a score of 0.784 on 20,000 slices that include both labeled and pseudo-labeled data, thus surpassing its supervised baseline of 0.755. This study presents a conceptual framework for the integration of genomic biomarkers with CT-derived features, facilitating the development of future multimodal, biologically informed CAD systems. The proposed semi-supervised Inf-Net framework improves CT-based lung nodule assessment and lays the groundwork for flexible multi-omics diagnostic models.

q-bio.QM↗

arXiv2018

Reducing Conservatism in Model-Invariant Safety-Preserving Control of Propofol Anesthesia Using Falsification

This work provides a formalized model-invariant safety system for closed-loop anesthesia that uses feedback from measured data for model falsification to reduce conservatism. The safety system maintains predicted propofol plasma concentrations, as well as the patient's blood pressure, within safety bounds despite uncertainty in patient responses to propofol. Model-invariant formal verification is used to formalize the safety system. This technique requires a multi-model description of model-uncertainty. Model-invariant verification considers all possible dynamics of an uncertain system, and the resulting safety system may be conservative for systems that do not exhibit the worst-case dynamical response. In this work, we employ model falsification to reduce conservatism of the model-invariant safety system. Members of a model set that characterizes model- uncertainty are falsified if discrepancy between predictions of those models and measured responses of the uncertain system is established, thereby reducing model uncertainty. We show that including falsification in a model-invariant safety system reduces conservatism of the safety system.

eess.SY↗