  Endomicroscopy is indispensable for minimally invasive diagnostics in
clinical 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 vivo
diagnosis. However, the inherent honeycomb-artifacts of coherent fiber bundles
(CFB) reduce the resolution and limit the clinical applications. We propose an
end-to-end lensless fiber imaging scheme toward intraoperative real-time cancer
diagnosis. The framework includes resolution enhancement and classification
networks that use single-shot fiber bundle images to provide both
high-resolution images and tumor diagnosis result. The well-trained resolution
enhancement network not only recovers high-resolution features beyond the
physical limitations of CFB, but also helps improving tumor recognition rate.
Especially for glioblastoma, the resolution enhancement network helps
increasing the classification accuracy from 90.8% to 95.6%. The novel technique
can enable histological real-time imaging through lensless fiber endoscopy and
is promising for rapid and minimal-invasive intraoperative diagnosis in
clinics.
