Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma.

Background: Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC.

Methods: We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status.

Experimental investigation of the reliability issue of RRAM based on high resistance state conduction.

In this paper, reliability issues of robust HfO(x)-based RRAM are experimentally investigated in terms of cycling ageing, temperature impact and voltage acceleration. All reliability issues can be estimated by the conduction of the high resistance state (HRS). The conduction current of the HRS exponentially increases as the square root of the applied voltage, which is well explained by 'quasi-Poole-Frenkel-type' trap assistant tunneling.