Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability.

Objectives: To evaluate a fully automatic deep learning system to detect and segment clinically significant prostate cancer (csPCa) on same-vendor prostate MRI from two different institutions not contributing to training of the system.

Materials And Methods: In this retrospective study, a previously bi-institutionally validated deep learning system (UNETM) was applied to bi-parametric prostate MRI data from one external institution (A), a PI-RADS distribution-matched internal cohort (B), and a csPCa stratified subset of single-institution external public challenge data (C). csPCa was defined as ISUP Grade Group ≥ 2 determined from combined targeted and extended systematic MRI/transrectal US-fusion biopsy.

Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones.

Deep-learning (DL) noise reduction techniques in computed tomography (CT) are expected to reduce the image noise while maintaining the clinically relevant information in reduced dose acquisitions. This study aimed to assess the size, attenuation, and objective image quality of reno-ureteric stones denoised using DL-software in comparison to traditionally reconstructed low-dose abdominal CT-images and evaluated its clinical impact. In this institutional review-board-approved retrospective study, 45 patients with renal and/or ureteral stones were included.

Epitaxial Growth of Crystalline CaF on Silicene.

Silicene is one of the most promising two-dimensional (2D) materials for the realization of next-generation electronic devices, owing to its high carrier mobility and band gap tunability. To fully control its electronic properties, an external electric field needs to be applied perpendicularly to the 2D lattice, thus requiring the deposition of an insulating layer that directly interfaces silicene, without perturbing its bidimensional nature. A promising material candidate is CaF, which is known to form a quasi van der Waals interface with 2D materials as well as to maintain its insulating properties even at ultrathin scales.

Optical Signatures of Dirac Electrodynamics for hBN-Passivated Silicene on Au(111).

The allotropic affinity for bulk silicon and unique electronic and optical properties make silicene a promising candidate for future high-performance devices compatible with mature complementary metal-oxide-semiconductor technology. However, silicene's outstanding properties are not preserved on its most prominent growth templates, due to strong substrate interactions and hybridization effects. In this letter, we report the optical properties of silicene epitaxially grown on Au(111).

Highly Biaxially Strained Silicene on Au(111).

Many of graphene's remarkable properties arise from its linear dispersion of the electronic states, forming a Dirac cone at the K points of the Brillouin zone. Silicene, the 2D allotrope of silicon, is also predicted to show a similar electronic band structure, with the addition of a tunable bandgap, induced by spin-orbit coupling. Because of these outstanding electronic properties, silicene is considered as a promising building block for next-generation electronic devices.