Saurabh Joshi

This study enhances CAR-T cell migration through synthetic cytokine receptors, reducing metastasis in TNBC mouse models.

CODAvision provides a user-friendly interface for rapid, customizable medical image segmentation using deep learning, enabling non-experts to train and evaluate models across diverse imaging modalities.

This paper introduces SMILE, a diffusion model for converting H&E stains to multiplex IHC images, showing superior performance in pancreatic islet analysis and breast cancer stain conversion.

The paper uses 3D imaging to analyze vascular and nervous systems in embryos, finding shared structural principles but distinct architectures shaped by biophysical constraints.

This study shows hyper-migratory CAR T cells reduce ovarian cancer metastasis and improve survival in mice.

The paper presents a 3D multi-omic framework to detect and characterize ovarian cancer precursors in fallopian tubes, identifying TP53 mutations in microscopic lesions.

This paper develops a 3D multi-compartment assembloid that mimics human endometrial tissue and its menstrual cycle phases, enabling study of compartment-specific hormonal responses and paracrine signaling.

This study shows that 3D assessments are necessary to accurately determine tissue composition and neoplastic content, as 2D methods under-sample spatial heterogeneity.

The paper integrates 3D imaging with nuclear segmentation to analyze nuclear morphology in human tissue, revealing spatial patterns and interactions in disease progression.

This paper uses a generative AI model to improve 3D tissue imaging by interpolating and restoring images, enhancing resolution and reducing artifacts across multiple modalities and species.