EndoViT: pretraining vision transformers on a large collection of endoscopic images.

Purpose: Automated endoscopy video analysis is essential for assisting surgeons during medical procedures, but it faces challenges due to complex surgical scenes and limited annotated data. Large-scale pretraining has shown great success in natural language processing and computer vision communities in recent years. These approaches reduce the need for annotated data, which is of great interest in the medical domain.

Comparative immuno-Cerenkov luminescence and -PET imaging enables detection of PSMA tumors in mice using Cu-radiolabeled monoclonal antibodies.

Here, we describe immuno-Cerenkov luminescence imaging (immuno-CLI) with a specific monoclonal antibody-based tracer for the detection of prostate tumors, which is used in preclinical positron emission tomography (PET) imaging. As PET isotopes generate a continuous spectrum of light in the ultraviolet/visible (UV/vis) wavelength range (Cerenkov luminescence, CL) in dielectric materials and consequently inside living tissues, these isotopes can also be detected by luminescence imaging performed with optical imaging (OI) systems. Imaging tumors with tracers that are specifically binding to a tumor-associated antigen can increase diagnostic accuracy, enables monitoring of treatment efficacy, and can be advantageous compared to radiolabeled small molecules used in PET-oncology such as 2-deoxy-2-[F]-fluoro-D-glucose ([F]FDG; glucose metabolism) or [C]choline (membrane synthesis) which was used to image prostate cancer.