Abstract: Disclosed is a computer-implemented three-dimensional image classification system (CIS) for processing and/or analyzing non-contrast computed tomography (CT) medical imaging data. The CIS is a deep neural network containing multiple Convolutional Block Attention Module (CBAM) blocks, which contain convolutional layers for feature extraction followed by CBAMs. The CBAM applies channel attention to highlight more relevant features and spatial attention to focus on more important regions. Max pooling layers operably link adjacent pairs of CBAM blocks. The output of the final CBAM block is passed to two terminal fully connected layers to generate a diagnosis. This classification system can be used to perform efficient diagnosis of hepatocellular carcinoma using solely non-contrast CT images, with diagnostic performance comparable to that of a radiologist using the current LIRADS system.

Abstract: The present disclosure provides a two-dimensional scalable superconducting qubit structure and a method for controlling a cavity mode thereof. The two-dimensional scalable superconducting qubit structure includes: a superconducting qubit chip comprising a plurality of two-dimensionally distributed and scalable qubits; a capacitor part of each of the qubits has at least five arms distributed two-dimensionally, two of the at least five arms in each qubit are respectively connected with a read coupling circuit and a control circuit, and the other at least three arms are coupled with adjacent qubits through a coupling cavity.