Anna Mühlig | Searcharxiv

arXiv2026

Quantum scanning synthetic optical holography

Synthetic optical holography (SOH) introduced holographic reconstruction into scanning optical microscopy, enabling quantitative phase imaging with sequential acquisition and point detection. Here, we extend this concept to the quantum regime by implementing SOH within quantum imaging with undetected light (QIUL). By integrating a controlled synthetic phase carrier into a scanning QIUL implementation, we retrieve amplitude and phase images of objects probed by mid-infrared (MIR) photons while detecting only their visible partners. We demonstrate the method on binary, transparent, and biological samples, showing complex-field reconstruction in a scanning quantum imaging system. This approach decouples spatial resolution from photon-pair spatial correlations and establishes a route toward diffraction-limited, label-free MIR phase imaging with visible-wavelength detection.

physics.optics↗

arXiv2026

RamanSeg: Interpretability-driven Deep Learning on Raman Spectra for Cancer Diagnosis

Histopathology, the current gold standard for cancer diagnosis, involves the manual examination of tissue samples after chemical staining, a time-consuming process requiring expert analysis. Raman spectroscopy is an alternative, stain-free method of extracting information from samples. Using nnU-Net, we trained a segmentation model on a novel dataset of spatial Raman spectra aligned with tumour annotations, achieving a mean foreground Dice score of 80.9%, surpassing previous work. Furthermore, we propose a novel, interpretable, prototype-based architecture called RamanSeg. RamanSeg classifies pixels based on discovered regions of the training set, generating a segmentation mask. Two variants of RamanSeg allow a trade-off between interpretability and performance: one with prototype projection and another projection-free version. The projection-free RamanSeg outperformed a U-Net baseline with a mean foreground Dice score of 67.3%, offering a meaningful improvement over a black-box training approach.

eess.IV↗