MediMath Science

Before beginning this blog, I should acknowledge that I do not have the ability to speak with complete confidence about the current confrontation between the U.S. and Iran. Those who are not direct participants in the conflict cannot fully understand the deeper motives, internal calculations, and hidden tensions between the parties. As an outside observer, therefore, I am not in a position to make definitive judgments. Much of the true structure of the conflict is neither visible nor easily knowable, and even a logically coherent model may fail when the underlying incentives, information, or constraints change unexpectedly. Most media reports portray the current confrontation between the U.S. and Iran as a struggle between good and evil, or as the product of the greed of a few actors. While this framing attracts attention, it is analytically weak. Consequently, individual investors influenced by such narratives may misinterpret the prospects for war and make costly financial decisi...

The performance of supervised learning in digital medical imaging modalities such as ultrasound and low-dose CBCT depends critically on the availability of paired datasets. These datasets must capture variability across patients, anatomical structures, and disease presentations, while providing accurate and consistent labels aligned with the measured images. Diagnostic tasks—including segmentation and detection—are particularly dependent on such paired data, requiring reliable annotations such as lesion localization, bounding regions, and clinically meaningful diagnostic labels. Consequently, robust model training requires large-scale datasets with high-quality annotations spanning diverse patient populations. However, in real clinical settings, such high-quality paired datasets are often unavailable due to the limited representation of abnormal cases, the absence of ground truth, inter-observer variability in annotations, patient-specific image heterogeneity, and the inherent variabil...