Objective: This study aims to develop a deep learning (DL)-based multimodal framework that integrates magnetic resonance imaging (MRI), clinical, and laboratory data to predict programmed death ligand ...

Abstract: This research was conducted to compare image processing accuracy in detecting lung cancer using two feature extraction methods: grey-level co-occurrence matrix feature extraction and local ...

Abstract: Current multi-band SAR feature extraction methods often fail to adequately exploit intra-band details and inter-band spectral relationships, limiting their classification performance. To ...

Researchers at Tsinghua University developed the Optical Feature Extraction Engine (OFE2), an optical engine that processes data at 12.5 GHz using light rather than electricity. Its integrated ...

The openfeature python sdk supports both sync and async evaluation. However, the current goff Python provider only provides sync evaluation methods. The issue is, with Python, due to the existence of ...

The proposed optical computing chip enables high-speed, parallel processing for quantitative trading with unprecedented low latency, accelerating the crucial and demanding step of feature extraction.

A research team from Tsinghua University has developed an optical computing system that dramatically reduces latency in feature extraction - one of the most critical stages in real-time data ...