Information Geometric Approaches for Patient-Specific Test-Time Adaptation of Deep Learning Models for Semantic Segmentation.

The test-time adaptation (TTA) of deep-learning-based semantic segmentation models, specific to individual patient data, was addressed in this study. The existing TTA methods in medical imaging are often unconstrained, require anatomical prior information or additional neural networks built during training phase, making them less practical, and prone to performance deterioration. In this study, a novel framework based on information geometric principles was proposed to achieve generic, off-the-shelf, regularized patient-specific adaptation of models during test-time.

Recursive feature elimination for biomarker discovery in resting-state functional connectivity.

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons.

An early respiratory distress detection method with Markov models.

A method for early detection of respiratory distress in hospitalized patients which is based on a multi-parametric analysis of respiration rate (RR) and pulse oximetry (SpO2) data trends to ascertain patterns of patient instability pertaining to respiratory distress is described. Current practices of triggering caregiver alerts are based on simple numeric threshold breaches of SpO2. The pathophysiological patterns of respiratory distress leading to in-hospital deaths are much more complex to be detected by numeric thresholds.