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Ming Zhao

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10 papers

2 author rows

JBHI Journal 2025 Journal Article

An Arbitrary-Scale Super-Resolution Network for Multi-Contrast MRI With Permuted Cross-Attention

Ming Zhao
Jia Fang
Boyang Chen

In magnetic resonance imaging (MRI), low-resolution (LR) images often hamper clinical diagnosis and research due to constraints in imaging conditions and technology limitations. Recent studies in super-resolution (SR) reconstruction of multi-contrast MRI have shown promise by leveraging the complementary information from different MRI contrasts. However, existing multi-contrast MRI SR techniques face several challenges: 1) a lack of pre-alignment precision can result in distorted reconstructions; 2) prevailing transformer network structures, with their smaller windows (e. g. , 8 × 8), struggle to effectively capture long-range dependencies and lack the ability to interact between different windows; and 3) current methods are limited to fixed integer scaling (e. g. , 2 ×, 3 ×, 4 ×), which limits flexibility and increases complexity in training and storage. To address these challenges, we propose a novel arbitrary-scale SR network for multi-contrast MRI. Specifically, our approach compensates for spatial misalignment between modalities through deformable registration module and employs permuted cross-attention transformer in MR images. In addition, we introduce a ref-scale ensemble implicit attention module that better integrates high-frequency information from reference images and enables arbitrary-scale upsampling. Extensive experiments on two publicly available MRI datasets validate the superiority of our method in multi-contrast MRI SR, demonstrating its significant potential in clinical applications.

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IJCAI Conference 2025 Conference Paper

RTdetector: Deep Transformer Networks for Time Series Anomaly Detection Based on Reconstruction Trend

Xinhong Liu
Xiaoliang Li
Yangfan Li
Fengxiao Tang
Ming Zhao

Anomaly detection in multivariate time series data is critical across a variety of real-life applications. The predominant anomaly detection techniques currently rely on reconstruction-based methods. However, these methods often overfit the abnormal pattern and fail to diagnose the anomaly. Although some studies have attempted to prevent the incorrect fitting of anomalous data by enabling models to learn the trend of data variations, they fail to account for the dynamic nature of data distribution. This oversight can lead to the erroneous reconstruction of anomalies that do not exist. To address these challenges, we propose RTdetector, a Transformer-based time series anomaly detection model leveraging reconstruction trends. RTdetector employs a novel global attention mechanism based on reconstruction trends to learn distinguishable attention from the original sequence, thereby preserving the global trend information intrinsic to the time series. Additionally, it incorporates a self-conditioning transformer, based on reconstruction trend enhancement to achieve superior predictive performance. Extensive experiments on four datasets demonstrate that RTdetector achieves state-of-the-art results in multivariate time series data anomaly detection. Our code is available at https: //github. com/CSUFUNLAB/RTdetector.

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ECAI Conference 2025 Conference Paper

SDQ-LLM: Sigma-Delta Quantization for 1-Bit LLMs of Any Size

Junhao Xia
Ming Zhao
Limin Xiao
Xiujun Zhang

Large language models (LLMs) face significant computational and memory challenges, making extremely low-bit quantization crucial for their efficient deployment. In this work, we introduce SDQ-LLM: Sigma-Delta Quantization for 1-bit LLMs of any size, a novel framework that enables extremely low-bit quantization of LLMs while preserving their linguistic reasoning capabilities. A distinctive feature of SDQ-LLM is the continuous adjustability of the Over-Sampling Ratio (OSR), enabling dynamic adaptation to memory or VRAM constraints by selecting fractional OSR (e. g. , 2. 5×) for an optimal trade-off between model size and accuracy. SDQ-LLM uses upsampling combined with Sigma-Delta Quantizer to binarize or ternarize LLMs’ weights, encoding high-precision parameters into 1-bit or 1. 58-bit representations, replacing the multiplication operations within linear layers with addition. This approach significantly enhances inference efficiency under extremely low-bit quantization. To further reduce the loss of quantization precision, we incorporate Hadamard-based weight smoothing prior to quantization, improving the stability and robustness of the weight representations. Furthermore, to fully leverage the continuity of the OSR and reduce precision loss, recognizing the correlation between quantization sensitivity and weight variance, we propose a fine-grained, layer- and linear-wise OSR allocation strategy, MultiOSR. This strategy distributes OSR both across layers and within each layer, based on weight variance and parameter scale. Finally, extensive experiments on OPT and LLaMA model families demonstrate that SDQ-LLM achieves a more efficient and high-precision performance even under highly aggressive low-OSR settings. Our code is available at https: //github. com/Dreamlittlecat/LLM-Quant-Factory.

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NeurIPS Conference 2025 Conference Paper

ToxicTextCLIP: Text-Based Poisoning and Backdoor Attacks on CLIP Pre-training

Xin Yao
Haiyang Zhao
Yimin Chen
Jiawei Guo
Kecheng Huang
Ming Zhao

The Contrastive Language-Image Pretraining (CLIP) model has significantly advanced vision-language modeling by aligning image-text pairs from large-scale web data through self-supervised contrastive learning. Yet, its reliance on uncurated Internet-sourced data exposes it to data poisoning and backdoor risks. While existing studies primarily investigate image-based attacks, the text modality, which is equally central to CLIP's training, remains underexplored. In this work, we introduce ToxicTextCLIP, a framework for generating high-quality adversarial texts that target CLIP during the pre-training phase. The framework addresses two key challenges: semantic misalignment caused by background inconsistency with the target class, and the scarcity of background-consistent texts. To this end, ToxicTextCLIP iteratively applies: 1) a background-aware selector that prioritizes texts with background content aligned to the target class, and 2) a background-driven augmenter that generates semantically coherent and diverse poisoned samples. Extensive experiments on classification and retrieval tasks show that ToxicTextCLIP achieves up to 95. 83\% poisoning success and 98. 68% backdoor Hit@1, while bypassing RoCLIP, CleanCLIP and SafeCLIP defenses. The source code can be accessed via https: //github. com/xinyaocse/ToxicTextCLIP/.

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JBHI Journal 2024 Journal Article

CFI-Net: A Choquet Fuzzy Integral Based Ensemble Network With PSO-Optimized Fuzzy Measures for Diagnosing Multiple Skin Diseases Including Mpox

Sohaib Asif
Ming Zhao
Yangfan Li
Fengxiao Tang
Yusen Zhu

In the domain of medical diagnostics, precise identification of various skin and oral diseases is vital for effective patient care. In particular, Mpox is a potentially dangerous viral disease with zoonotic origins, capable of human-to-human transmission, underscoring the urgency of precise diagnostic methods for timely intervention. This paper introduces a novel approach named the Choquet Fuzzy Integral-based Ensemble (CFI-Net) for accurate classification of skin diseases, with a specific emphasis on detecting Mpox, foot ulcers, and various mouth and oral diseases. Our methodology begins with Transfer Learning, enhancing the classification capabilities of base classifiers (DenseNet169, MobileNetV1 and DenseNet201) by incorporating additional layers. Subsequently, we aggregate the prediction scores from each base classifier using the Choquet fuzzy integral (CFI) to derive the final predicted labels, thus ensuring dynamic and robust predictions. Fuzzy measures, a crucial component of this fuzzy integral-based ensemble method, are typically determined through manual experimentation in previous approaches. However, in our study, we have tackled the challenge of manual tuning by employing meta-heuristic optimization algorithm to precisely configure the fuzzy measures for optimal performance. A rigorous evaluation is conducted on four publicly available datasets, encompassing two Mpox datasets, a foot ulcer dataset, and a mouth and oral disease dataset. The experiments reveal the remarkable effectiveness of CFI-Net in significantly improving disease classification accuracy. Additionally, we employ Grad-CAM analysis to provide insights into the decision-making processes of our models. Our findings underscore the exceptional performance of CFI-Net, achieving accuracy rates of 98. 06% and 94. 81% for Mpox detection, 99. 06% for foot ulcer detection, and an impressive 99. 61% for mouth and oral disease classification. This research not only contributes to the advancement of disease diagnosis but also demonstrates the effectiveness of ensemble learning techniques coupled with fuzzy integral-based fusion in enhancing diagnostic accuracy.

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EAAI Journal 2024 Journal Article

Machinery degradation trend prediction considering temporal distribution discrepancy between degradation stages

Shudong Ou
Ming Zhao
Hao Wu
Yue Zhang
Sen Li

Catering to the rapidly growing of smart manufacturing, recent years have witnessed that a great number of degradation trend prediction techniques were developed with the desire to guarantee the reliability and safety of machinery. Most existing studies assume that the future degradation trends follow similar data distributions to the degradation features fed for modeling, which may be challenging to satisfy in realistic scenarios. To address this issue, distribution discrepancy aligning-oriented domain adaptation was proposed. However, the stage-specific properties of the degradation process are often ignored, which may not be conducive. to maintain consistency in the distribution within the degradation stage and to evaluate the temporal distribution discrepancy in the degradation process. Therefore, this study presents a performance degradation trend prediction methodology that incorporates considerations of the temporal distribution discrepancy between degradation stages to overcome these challenges. Specifically, an adaptive degradation stage characterization (AdaDSC) strategy is designed to partition the degradation feature sequence into multiple degradation stages, ensuring that their distributions are as diverse as possible. Building upon this, a distribution discrepancy evaluation gated recurrent unit (DDE-GRU) module is introduced. It aims to mitigate the effects of distribution gaps while capturing the temporal dependencies within the data. The feasibility and effectiveness of the proposed methodology are verified through experiments conducted on three publicly available run-to-failure datasets, as well as an engineering dataset collected from the main bearing of a wind turbine. These experiments demonstrate the potential and practicality of the proposed methodology in accurately predicting performance degradation trends.

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JBHI Journal 2022 Journal Article

Multi-Dimensional Feature Combination Method for Continuous Blood Pressure Measurement Based on Wrist PPG Sensor

Pan Yao
Ning Xue
Siyuan Yin
Changhua You
Yusen Guo
Yi Shi
Tiezhu Liu
Lei Yao

The cuff-less blood pressure (BP) monitoring method based on photoplethysmo- gram (PPG) makes it possible for long-term BP monitoring to prevent and treat cardiovascular and cerebrovascular events. In this paper, a portable BP prediction system based on feature combination and artificial neural network (ANN) is implemented. The robustness of the model is improved from three aspects. Firstly, an adaptive peak extraction algorithm was used to improve the accuracy of peaks and troughs detection. Secondly, multi-dimensional features were extracted and fused, including three groups of PPG-based features and one group of demographics-based features. Finally, a two-layer feedforward artificial neural networks algorithm was used for regression. Thirty-three subjects distributed in the three BP groups were recruited. The proposed method passed the European Society of Hypertension International Protocol revision 2010 (ESP-IP2). Experimental results show that the proposed method exhibits good accuracy for a diverse population with an estimation error of −0. 07 ± 4. 47 mmHg for SBP and 0. 00 ± 3. 61 mmHg for DBP. Moreover, the model tracked the BP of two subjects for half a month, laying the foundation work for daily BP monitoring. This work will contribute to the long-term wellness management and rehabilitation process, enabling timely detection and improvement of the user's physical health.

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IROS Conference 2022 Conference Paper

RGB-X Classification for Electronics Sorting

Abhimanyu
Tejas Zodage
Umesh Thillaivasan
Xinyue Lai
Rahul Chakwate
Javier Santillan
Emma Oti
Ming Zhao

Effectively disassembling and recovering materials from waste electrical and electronic equipment (WEEE) is a critical step in moving global supply chains from carbon-intensive, mined materials to recycled and renewable ones. Conventional recycling processes rely on shredding and sorting waste streams, but for WEEE, which is comprised of numerous dissimilar materials, we explore targeted disassembly of numerous objects for improved material recovery. Many WEEE objects share many key features and therefore can look quite similar, but their material composition and internal component layout can vary, and thus it is critical to have an accurate classifier for subsequent disassembly steps for accurate material separation and recovery. This work introduces RGB-X, a multi-modal image classification approach, that utilizes key features from external RGB images with those generated from X-ray images to accurately classify electronic objects. More specifically, this work develops Iterative Class Activation Mapping (iCAM), a novel network architecture that explicitly focuses on the finer-details in the multi-modal feature maps that are needed for accurate electronic object classification. In order to train a classifier, electronic objects lack large and well annotated X-ray datasets due to expense and need of expert guidance. To overcome this issue, we present a novel way of creating a synthetic dataset using domain randomization applied to the X-ray domain. The combined RGB-X approach gives us an accuracy of 98. 6% on 10 generations of modern smartphones, which is greater than their individual accuracies of 89. 1% (RGB) and 97. 9% (X-ray) independently. We provide experimental results 1 3 3 Experimental work done at Biorobotics Lab, Robotics Institute, Carnegie Mellon University to corroborate our results.

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IROS Conference 2021 Conference Paper

CLMM-Net: Robust Cascaded LiDAR Map Matching based on Multi-Level Intensity Map

Kai Chen 0028
Lei He
Xiaofeng Wang
Yuqian Liu
Ming Zhao

LiDAR map matching(LMM) is a critical localization technique in autonomous driving while existing methods have problems in terms of both accuracy and robustness when driving in the scenes with poor structure information (e. g. highways). This paper put forward a multi-level intensity map based cascaded network for LiDAR map matching in autonomous driving. The network uses an effective multi-level intensity map representation to compactly encode the appearance and structure information of point clouds, which effectively reduce the position ambiguity in structure-less scenarios. Besides, this method leverages the multi-scale nature of deep neural networks and matches the online LiDAR observation with the offline map in a coarse-to-fine manner so as to balance the time-consuming and precision. Extensive experiments on diverse autonomous driving environments demonstrate the superiority of our proposed method over other existing state-of-the-art methods.

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ICRA Conference 2018 Conference Paper

ESD CYCLOPS: A New Robotic Surgical System for GI Surgery

Timo J. C. Oude Vrielink
Ming Zhao
Ara Darzi
George P. Mylonas

Gastrointestinal (GI) cancers account for 1. 5 million deaths worldwide. Endoscopic Submucosal Dissection (ESD) is an advanced therapeutic endoscopy technique with superior clinical outcome due to the minimally invasive and en bloc removal of tumours. In the western world, ESD is seldom carried out, due to its complex and challenging nature. Various surgical systems are being developed to make this therapy accessible, however, these solutions have shown limited operational workspace, dexterity, or low force exertion capabilities. The current paper shows the ESD CYCLOPS system, a bimanual surgical robotic attachment that can be mounted at the end of any flexible endoscope. The system is able to achieve forces of up to 46N, and showed a mean error of 0. 217mm during an elliptical tracing task. The workspace and instrument dexterity is shown by pre-clinical ex vivo trials, in which ESD is successfully performed by a GI surgeon. The system is currently undergoing pre-clinical in vivo validation.

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