Department Publications

Spatial transcriptomics is an emerging field that enables the identification of functional regions based on the spatial distribution of gene expression. Integrating this functional information present in transcriptomic data with structural data from histopathology images is an active research area with applications in identifying tumor substructures associated with cancer drug resistance. Current histopathology-spatial-transcriptomic region segmentation methods suffer due to either making spatial transcriptomics prominent by using histopathology features just to assist processing spatial transcriptomics data or using vanilla contrastive learning that make histopathology images prominent due to only promoting common features losing functional information. In both extremes, the model gets either lost in the noise of spatial transcriptomics or overly smoothed, losing essential information. Thus, we propose our novel …

Neonatal jaundice, caused by elevated bilirubin levels, requires timely diagnosis to prevent severe complications. Existing deep learning approaches primarily focus on binary classification of jaundice from visual data, but do not provide quantitative bilirubin estimation. A binary decision alone is insufficient for clinical management, where treatment thresholds depend on precise bilirubin levels. This paper presents a deep-learning-based contactless framework for neonatal jaundice assessment using image-based analysis. The proposed lightweight multi-stage pipeline integrates eye-region obfuscation, skin segmentation, and deep-learning-based image classification and bilirubin estimation. The system first performs jaundice classification; upon positive detection, a regression model is invoked to estimate bilirubin. Furthermore, image-based neonatal screening raises privacy concerns, as facial images of …

Diabetes is associated with progressive microvascular remodelling, commonly assessed using retinal imaging, yet alternative non-invasive vascular biomarkers remain underexplored. Conjunctival vessels are directly visible and may reflect systemic microvascular changes; however, reliable quantification is challenged by the need to preserve vascular topology during segmentation. In this work, we introduce a curated conjunctival imaging dataset with diameter-specific vessel annotations and demonstrate that topology-preserving vessel extraction is essential for robust tortuosity estimation. We develop a deep learning framework that prioritises vascular continuity and geometric fidelity while selectively segmenting clinically relevant vessel calibres, integrating multi-scale contextual modelling and attention-guided feature fusion to reduce fragmentation without post-processing. Compared with multiple state-of-the-art …