Mihail Popescu

Noah Marchal, William E. Janes, Mihail Popescu et al.

Clinical monitoring of functional decline in ALS relies on periodic assessments that may miss critical changes occurring between visits. To address this gap, semi-supervised regression models were developed to estimate rates of decline in a case series cohort by targeting ALSFRS- R scale trajectories with continuous in-home sensor monitoring data. Our analysis compared three model paradigms (individual batch learning and cohort-level batch versus incremental fine-tuned transfer learning) across linear slope, cubic polynomial, and ensembled self-attention pseudo-label interpolations. Results revealed cohort homogeneity across functional domains responding to learning methods, with transfer learning improving prediction error for ALSFRS-R subscales in 28 of 32 contrasts (mean RMSE=0.20(0.04)), and individual batch learning for predicting the composite scale (mean RMSE=3.15(1.25)) in 2 of 3. Self-attention interpolation achieved the lowest prediction error for subscale-level models (mean RMSE=0.19(0.06)), capturing complex nonlinear progression patterns, outperforming linear and cubic interpolations in 20 of 32 contrasts, though linear interpolation proved more stable in all ALSFRS-R composite scale models (mean RMSE=0.23(0.10)). We identified distinct homogeneity-heterogeneity profiles across functional domains with respiratory and speech exhibiting patient-specific patterns benefiting from personalized incremental adaptation, while swallowing and dressing functions followed cohort-level trajectories suitable for transfer models. These findings suggest that matching learning and pseudo-labeling techniques to functional domain-specific homogeneity-heterogeneity profiles enhances predictive accuracy in ALS progression tracking. Integrating adaptive model selection within sensor monitoring platforms could enable timely interventions and scalable deployment in future multi-center studies.

Muhammad Aminul Islam, Bryce Murray, Andrew Buck et al.

While most deep learning architectures are built on convolution, alternative foundations like morphology are being explored for purposes like interpretability and its connection to the analysis and processing of geometric structures. The morphological hit-or-miss operation has the advantage that it takes into account both foreground and background information when evaluating target shape in an image. Herein, we identify limitations in existing hit-or-miss neural definitions and we formulate an optimization problem to learn the transform relative to deeper architectures. To this end, we model the semantically important condition that the intersection of the hit and miss structuring elements (SEs) should be empty and we present a way to express Don't Care (DNC), which is important for denoting regions of an SE that are not relevant to detecting a target pattern. Our analysis shows that convolution, in fact, acts like a hit-miss transform through semantic interpretation of its filter differences. On these premises, we introduce an extension that outperforms conventional convolution on benchmark data. Quantitative experiments are provided on synthetic and benchmark data, showing that the direct encoding hit-or-miss transform provides better interpretability on learned shapes consistent with objects whereas our morphologically inspired generalized convolution yields higher classification accuracy. Last, qualitative hit and miss filter visualizations are provided relative to single morphological layer.