Zhengtao Yu

An interpretable deep learning model based on enhanced CT predicts the recurrence of non-muscle-invasive bladder cancer within 2 years

Zhengtao Yu, Zhe Hao Wu, Jian Huang, Zhi Fei Zeng et autres

Accurate risk stratification for non-muscle-invasive bladder cancer (NMIBC) recurrence is essential for personalized treatment. This study aimed to develop and validate a contrast-enhanced computed tomography (CT)-based model for predicting postoperative NMIBC recurrence within two years. We retrospectively enrolled 192 patients with NMIBC …

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Interpretable machine learning model of contrast-enhanced CT radiomics for predicting post-BCG recurrence in high-grade non-muscle-invasive bladder cancer

Xin Chang Zou, Zhengtao Yu, Yu Yang Yuan, Hai Chao Chao et autres

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Dual-visual dynamic decoding for multimodal machine translation

Jingbo Zhu, Zhengtao Yu

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Interpretable machine learning using CT radiomics predicts pathological upgrading after secondary resection in non-muscle-invasive bladder cancer

Xu-Guang Rao, Xin Chang Zou, Zhengtao Yu, Yu Yang Yuan et autres

BACKGROUND AND PURPOSE: Repeat transurethral resection (ReTUR) is essential for reducing residual and recurrent non-muscle-invasive bladder cancer (NMIBC). Pathological upstaging after ReTUR significantly influences prognosis. This study aimed to develop an interpretable machine learning model using CT radiomics to predict the risk …

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HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy

Fan Xu, Xinyu Hu, Zhengtao Yu, Lin Li et autres

Fan Xu, Xinyu Hu, Zhenghan Yu, Li Lin, Xu Zhang, Yang Zhang, Wei Zhou, Jinjie Gu, Xiaojun Wan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track). 2026.

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A radiomics-driven machine learning model for predicting bladder cancer prognosis identifies genes associated with radiomic features

Xin Chang Zou, Bin Wang, Zhengtao Yu, Hai Chao Chao et autres

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Ensemble machine learning models for predicting bone metastasis in bladder cancer

Zhengtao Yu, Xiang Xu, Xin Chang Zou, Po‐Lan Su et autres

Background and purpose The occurrence of bone metastasis (BM) in advanced bladder cancer (BC) often signifies a poor prognosis. Currently, the accurate prediction of BM in BC remains a challenge. This study develops predictive models using machine learning algorithms to predict bladder …

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Predictive value of the stone-free rate after percutaneous nephrolithotomy based on multiple machine learning models

Zhao Rong Liu, Zhengtao Yu, Jie Zhou, Jian Huang

Purpose: This study aimed to develop three types of machine learning (ML) models based on gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) to explore their predictive value for the stone-free rate after percutaneous nephrolithotomy (PCNL). Patients …

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Design and research of a three-fingered flexible cabledriven robotic hand

Mengjie He, Cong Huang, Zhengtao Yu

Abstract Underactuated robotic hands are low-cost, highly adaptive, and less damaging to objects during grasping. This paper proposes designing a flexible underactuated robotic hand based on tendon-driven mechanisms to enhance grasping stability. It establishes a D-H parameter model for single-finger kinematic analysis, …

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CFunModel: A "Funny" Language Model Capable of Chinese Humor Generation and Processing

Zhengtao Yu, Xinyu Hu, Xiaojun Wan

Humor plays a significant role in daily language communication. With the rapid development of large language models (LLMs), natural language processing has made significant strides in understanding and generating various genres of texts. However, most LLMs exhibit poor performance in generating and …

A Dual-Perspective NLG Meta-Evaluation Framework with Automatic Benchmark and Better Interpretability

Xinyu Hu, Mingqi Gao, Lin Li, Zhengtao Yu et autres

In NLG meta-evaluation, evaluation metrics are typically assessed based on their consistency with humans. However, we identify some limitations in traditional NLG meta-evaluation approaches, such as issues in handling human ratings and ambiguous selections of correlation measures, which undermine the effectiveness of …

A Dual-Perspective NLG Meta-Evaluation Framework with Automatic Benchmark and Better Interpretability

Xinyu Hu, Mingqi Gao, Lin Li, Zhengtao Yu et autres

In NLG meta-evaluation, evaluation metrics are typically assessed based on their consistency with humans.However, we identify some limitations in traditional NLG meta-evaluation approaches, such as issues in handling human ratings and ambiguous selections of correlation measures, which undermine the effectiveness of meta-evaluation.In …

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