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Explainable medical visual question answering via chain of evidence

Knowledge-Based Systems 2025年 324卷

作者： Chen Qiu Ke Huang Zhiqiang Xie Maofu Liu Jinguang Gu Xiaofen Zong School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430081 China Department of Psychiatry Renmin Hospital of Wuhan University Wuhan 430060 China

Medical Visual Question Answering (MedVQA) has delivered success in healthcare, aiming to answer questions about clinical findings from medical images. However, developing models that are evidence-based and can precisely interpret decision-making with medical knowledge remains a crucial objective. To address these challenges, we reframe the MedVQA problem as a task of generating evidence, which is intrinsically consistent with the medical diagnostic interaction process. We introduce Med-CoE, a framework that leverages the chain-of-thought capacity of LLMs along with medical knowledge sources to address the explanation gap in MedVQA tasks. Med-CoE establishes an evidence verification and interpretation mechanism under a three-stage training process with medical knowledge grounding. We pre-train our model on the PMC-OA corpus and generate auto-labeling Chain of Evidences via LLMs prompt and then fine-tune Med-CoE on two benchmarks. Experimental findings show that Med-CoE outperforms state-of-the-art methods by a large margin, e.g., 10.7% on direct-answer task of PMC-VQA and 4.0% on SLAKE.

关键词： Chain of evidence Large language model Medical visual question answering

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Terminal Bulb Segmentation of Caenorhabditis Elegans under Small Samples Based on Two-stage U-Net Network 2019

Terminal Bulb Segmentation of Caenorhabditis Elegans under S...

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Proceedings of the 2019 3rd International Conference on computer science and Artificial Intelligence

作者： Si Cao Jun Liu Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Department of Computer Science and Technology Wuhan University of Science and technology Wuhan China

ISBN: (纸本)9781450376273

Convolutional neural network based on machine learning has become one of the most popular methods of image recognition and segmentation, but it needs huge data samples to get better results. In this study, based on U-Net network, a two-stage convolution neural network method for automatic segmentation of Terminal bulb of Caenorhabditis elegans in small samples was proposed. The method solves the problem that traditional single-stage network cannot be implemented in small samples. The Dice coefficient reaches 89.5%, which is higher than that of the baseline U-Net method.

关键词： Convolutional Neural Network computer Vision U-Net Caenorhabditis elegans

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Ultrasound vascular image segmentation based on full convolutional aggregation neural network 2019

Ultrasound vascular image segmentation based on full convolu...

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Proceedings of the 2019 3rd International Conference on computer science and Artificial Intelligence

作者： Yan Li Jun Liu Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Department of Computer Science and Technology Wuhan University of Science and technology Wuhan China

ISBN: (纸本)9781450376273

The concept of ultrasound therapy has been proposed long ago. However, most of the previous methods of ultrasound therapy destroy tissue through thermal effects and cause great damage to patients. In recent years, ultrasound cavitation therapy has caused extensive discussion and research due to its unique non-invasiveness. In order to achieve real-time access to the patient during treatment, the lesion area must be processed simultaneously and accurately. However, ultrasound images mostly have lower signal-to-noise ratio, contrast, and blurred edges. In order to solve the segmentation problem, this paper proposes a new segmentation model Du-net based on the full convolutional neural network. Under the premise of deepening the network depth to obtain more information, the Encoder-Decoder method and the layered aggregation mode are used to prevent the gradient explosion. Solving boundary segmentation problem by constructing joint network, In the case of a small data set, the data enhancement method using a suitable data set effectively increases the usable image features and achieves better results on the ultrasound blood vessel image.

关键词： layered aggregation mode Joint network ultrasonic therapy Full convolution network

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Ultrasound vascular image segmentation based on full convolutional aggregation neural network 3

Ultrasound vascular image segmentation based on full convolu...

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3rd International Conference on computer science and Artificial Intelligence, CSAI 2019 and its Workshop - The 11th International Conference on Information and Multimedia Technology, ICIMT 2019

作者： Li, Yan Liu, Jun Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Department of Computer Science and Technology Wuhan University of Science and technology Wuhan430070 China

ISBN: (纸本)9781450376273

The concept of ultrasound therapy has been proposed long ago. However, most of the previous methods of ultrasound therapy destroy tissue through thermal effects and cause great damage to patients. In recent years, ultrasound cavitation therapy has caused extensive discussion and research due to its unique noninvasiveness. In order to achieve real-time access to the patient during treatment, the lesion area must be processed simultaneously and accurately. However, ultrasound images mostly have lower signal-to-noise ratio, contrast, and blurred edges. In order to solve the segmentation problem, this paper proposes a new segmentation model Du-net based on the full convolutional neural network. Under the premise of deepening the network depth to obtain more information, the Encoder- Decoder method and the layered aggregation mode are used to prevent the gradient explosion. Solving boundary segmentation problem by constructing joint network, In the case of a small data set, the data enhancement method using a suitable data set effectively increases the usable image features and achieves better results on the ultrasound blood vessel image. © 2019 Association for computing Machinery.

关键词： Convolution

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Terminal bulb segmentation of caenorhabditis elegans under small samples based on two-stage u-net network 3

Terminal bulb segmentation of caenorhabditis elegans under s...

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3rd International Conference on computer science and Artificial Intelligence, CSAI 2019 and its Workshop - The 11th International Conference on Information and Multimedia Technology, ICIMT 2019

作者： Cao, Si Liu, Jun Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Department of Computer Science and Technology Wuhan University of Science and technology Wuhan430070 China

ISBN: (纸本)9781450376273

Convolutional neural network based on machine learning has become one of the most popular methods of image recognition and segmentation, but it needs huge data samples to get better results. In this study, based on U-Net network, a two-stage convolution neural network method for automatic segmentation of Terminal bulb of Caenorhabditis elegans in small samples was proposed. The method solves the problem that traditional singlestage network cannot be implemented in small samples. The Dice coefficient reaches 89.5%, which is higher than that of the baseline U-Net method. © 2019 Association for computing Machinery.

关键词： computer vision

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiuxia Shi Bin Hu Fuze Tian Qinglin Zhao Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University School of Computer and Communication Lanzhou University of Technology Ministry of Education Key Laboratory of Special Functional Materials and Structural Design (Lanzhou University) Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Engineering Research Center of Open Source Software and Real-Time System(Lanzhou University) Ministry of Education Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou University Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar.

关键词： Biomedical measurement Noise cancellation time measurement Doppler radar Recording Doppler effect Biosensors

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LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction

arXiv

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arXiv 2021年

作者： Lin, Yi Liu, Yanfei Chen, Hao Yang, Xin Ma, Kai Zheng, Yefeng Cheng, Kwang-Ting The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Shenzhen United Imaging Research Institute of Innovative Medical Equipment Real-Time Laboratory Shenzhen518048 China The Department of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Jarvis Research Center Tencent YouTu Lab Shenzhen518057 China

Radiation therapy treatment planning requires balancing the delivery of the target dose while sparing normal tissues, making it a complex process. To streamline the planning process and enhance its quality, there is a growing demand for knowledge-based planning (KBP). Ensemble learning has shown impressive power in various deep learning tasks, and it has great potential to improve the performance of KBP. However, the effectiveness of ensemble learning heavily depends on the diversity and individual accuracy of the base learners. Moreover, the complexity of model ensembles is a major concern, as it requires maintaining multiple models during inference, leading to increased computational cost and storage overhead. In this study, we propose a novel learning-based ensemble approach named LENAS, which integrates neural architecture search with knowledge distillation for 3D radiotherapy dose prediction. Our approach starts by exhaustively searching each block from an enormous architecture space to identify multiple architectures that exhibit promising performance and significant diversity. To mitigate the complexity introduced by the model ensemble, we adopt the teacher-student paradigm, leveraging the diverse outputs from multiple learned networks as supervisory signals to guide the training of the student network. Furthermore, to preserve high-level semantic information, we design a hybrid-loss to optimize the student network, enabling it to recover the knowledge embedded within the teacher networks. The proposed method has been evaluated on two public datasets, OpenKBP and AIMIS. Extensive experimental results demonstrate the effectiveness of our method and its superior performance to the state-of-the-art methods. Code: ***/hust-linyi/LENAS. © 2021, CC BY.

关键词： Students

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Retraction Note: Minimal key set of binary key-derivation tree in cloud storage

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Soft computing 2024年第2期28卷 1007-1007页

作者： Ren, Zhengwei Li, Xiaojuan Wang, Lina Xu, Shiwei Tong, Yan Tang, Jinshan School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Huazhong Agricultural University Wuhan China Department of Health Administration and Policy College of Health and Human Services George Mason University Fairfax USA

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A bilayer segmentation-recombination network for accurate segmentation of overlapping C. elegans

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Applied Soft computing 2025年 180卷

作者： Mengqian Ding Jun Liu Yang Luo Jinshan Tang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei 430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China School of Electronic Information Wuhan University of Science and Technology Wuhan Hubei 430065 China Health Informatics Department of Health Administration and Policy College of Public Health George Mason University Fairfax VA 22312 USA

Caenorhabditis elegans ( C. elegans ) is an excellent model organism because of its short lifespan and high degree of homology with human genes, and it has been widely used in a variety of human health and disease models. However, the segmentation of C. elegans remains challenging due to the following reasons: 1) the activity trajectory of C. elegans is uncontrollable, and multiple nematodes often overlap, resulting in blurred boundaries of C. elegans . This makes it impossible to clearly study the life trajectory of a certain nematode; and 2) in the microscope images of overlapping C. elegans , the translucent tissues at the edges obscure each other, leading to inaccurate boundary segmentation. To solve these problems, a Bilayer Segmentation-Recombination Network (BR-Net) for the segmentation of C. elegans instances is proposed. The network consists of three parts: A Coarse Mask Segmentation Module (CMSM), a Bilayer Segmentation Module (BSM), and a Semantic Consistency Recombination Module (SCRM). The CMSM is used to extract the coarse mask, and we introduce a United Attention Module (UAM) in CMSM to make CMSM better aware of nematode instances. The Bilayer Segmentation Module (BSM) segments the aggregated C. elegans into overlapping and non-overlapping regions. This is followed by integration by the SCRM, where semantic consistency regularization is introduced to segment nematode instances more accurately. Finally, the effectiveness of the method is verified on the C. elegans dataset. The experimental results show that BR-Net exhibits good competitiveness and outperforms other recently proposed segmentation methods in processing C. elegans occlusion images.

关键词： Amodal instance segmentation Attention mechanism Biological image analysis Caenorhabditis elegans Instance segmentation

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A Smart Roll Wear Check Scheme for Ensuring the Rolling Quality of Steel Plates 1

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4th International Conference on Smart computing and Communications, SmartCom 2019

作者： Zhang, Kui Zhou, Xiaohu He, Heng Wang, Yonghao Wang, Weihao Li, Huajian College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom

ISBN: (数字)9783030341398

ISBN: (纸本)9783030341381

Roll surface wear morphology directly affects the surface quality of steel plates and even affects the texture composition of plates and strip steel products. Using image processing methods to judge the wear state of a roll is low cost, easy to operate, and easy to realize an automatic smart data processing system. In this paper, we propose a Smart Roll Wear Check (SRWC) scheme for ensuring the rolling quality of steel plates. In the SRWC scheme, roll surface images in different wear stages are analyzed, from which seventeen dimension features are extracted. At the same time, the fractal theory is introduced to explore the relationship between fractal dimensions and roll wear degree. The results show that four characteristic parameters, such as roundness, equivalent area circle radius, second moment and texture entropy, and the fractal dimension can be used as effective parameters to quantitatively judge roll wear state. Lastly, a back-propagation (BP) neural network model for recognition and judgment for roll wear is established. It provides an experimental test to show that the five parameters as a quantitative evaluation for roll wear morphology are effective. By processing the data on the images, the SRWC scheme can demonstrate whether the roll needs to get off mill in time, so as to avoid the hidden danger of safety and ensure the rolling quality of the steel plate. © 2019, Springer Nature Switzerland AG.

关键词： Fractal dimension

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