Can Cui

Multi-modal learning with missing data for cancer diagnosis using histopathological and genomic data

2022 · Medical Imaging 2022: Computer-Aided Diagnosis

Multi-modal learning (e.g., integrating pathological images with genomic features) tends to improve the accuracy of cancer diagnosis and prognosis as compared to learning with a single modality. However, missing data is a common problem in …

Research on the security sharing method of power grid dispatching control data based on Alliance Blockchain and Zero-Knowledge Proof technology

2022 · 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)

With the widespread application of power system digital technology and the advancement of dispatch control cloud construction, the development of power grid regulation business presents the basic characteristics of many business participants, frequent business interactions, …

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

2023 · arXiv (Cornell University)

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific …

Optimization of dedicated domain text classification based on data augmentation using BERT generation pre-trained model

2023

Due to the excessive cost of data collection as well as annotation in dedicated domains, artificial intelligence model training is difficult to achieve optimality with insufficient data. To optimize this issue, a text generation data …

Improving Speaker Assignment in Speaker-Attributed ASR for Real Meeting Applications

2024

Past studies on end-to-end meeting transcription have focused on model architecture and have mostly been evaluated on simulated meeting data. We present a novel study aiming to optimize the use of a Speaker-Attributed ASR (SA-ASR) …

Fine-grained Multi-class Nuclei Segmentation with Molecular-empowered All-in-SAM Model

2025 · arXiv (Cornell University)

Purpose: Recent developments in computational pathology have been driven by advances in Vision Foundation Models, particularly the Segment Anything Model (SAM). This model facilitates nuclei segmentation through two primary methods: prompt-based zero-shot segmentation and the …

A Survey on Multimodal Large Language Models for Autonomous Driving

2024

With the emergence of Large Language Models (LLMs) and Vision Foundation Models (VFMs), multimodal AI systems benefiting from large models have the potential to equally perceive the real world, make decisions, and control tools as …