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Endomicroscopy is indispensable for minimally invasive diagnostics inclinical practice. For optical keyhole monitoring of surgical interventions,high-resolution fiber endoscopic imaging is considered to be very promising,especially in combination with label-free imaging techniques to realize in vivodiagnosis. However, the inherent honeycomb-artifacts of coherent fiber bundles(CFB) reduce the resolution and limit the clinical applications. We propose anend-to-end lensless fiber imaging scheme toward intraoperative real-time cancerdiagnosis. The framework includes resolution enhancement and classificationnetworks that use single-shot fiber bundle images to provide bothhigh-resolution images and tumor diagnosis result. The well-trained resolutionenhancement network not only recovers high-resolution features beyond thephysical limitations of CFB, but also helps improving tumor recognition rate.Especially for glioblastoma, the resolution enhancement network helpsincreasing the classification accuracy from 90.8% to 95.6%. The novel techniquecan enable histological real-time imaging through lensless fiber endoscopy andis promising for rapid and minimal-invasive intraoperative diagnosis inclinics.

Learned end-to-end high-resolution lensless fiber imaging toward intraoperative real-time cancer diagnosis