Automated Virtual Pathology Panels for ML-Assisted Interpretation of Mass Spectrometry Imaging
Year: 2026
Type: preprint
Abstract: Mass spectrometry imaging (MSI) records rich molecular spectra at each pixel, but pathology-oriented interpretation benefits from complementary views of the same tissue, loosely analogous to the multiple stains used in histopathology. We present an expert-aligned framework for constructing multi-view MSI panels. Soft Landmark Contrast Edges (SoLaCE) extracts molecular boundaries directly from high-dimensional spectra. Because standard structure-preservation metrics aligned poorly with rankings by pathologists, we combine luminance contrast and chromatic diversity with SpecEdge-Dice, a boundary-aware measure of agreement between visualization edges and SoLaCE boundaries. Parametric MiCS+LMC (pMiCS) uses a neural network trained on sub-sampled data to distill multiple MSI segmentations into a reusable spectral-to-RGB mapping, enabling rapid full-image inference, out-of-sample projection, and more consistent color semantics across aligned images. A concept-based interpretation procedure explains pMiCS outputs through sparse mixtures of spectral concepts, scoped as a model-output-equivalent summary rather than a molecular identification. We integrate these components into Virtual Pathology Panels , which use hyperparameter optimization to select fast, high-performing or spatially complementary views. This framework supports future workflows that combine automated, AI/ML-supported, morphology-oriented tissue assessment and molecular analysis within a single MSI acquisition. Teaser Virtual pathology panels transform MSI spectra into complementary views for scalable, interpretable tissue analysis.
Authors: Jacob Gildenblat, Jens Pahnke
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7171788391