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arXiv 2020年

作者： Jian, Caiqing Cheng, Xinyu Zhang, Jian Wang, Lihui Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang550025 China

Scalar coupling constant (SCC) plays a key role in the analysis of three-dimensional structure of organic matter, however, the traditional SCC prediction using quantum mechanical calculations is very time-consuming. To calculate SCC efficiently and accurately, we proposed a graph embedding local self-attention encoder (GELAE) model, in which, a novel invariant structure representation of the coupling system in terms of bond length, bond angle and dihedral angle was presented firstly, and then a local self-attention module embedded with the adjacent matrix of a graph was designed to extract effectively the features of coupling systems, finally, with a modified classification loss function, the SCC was predicted. To validate the superiority of the proposed method, we conducted a series of comparison experiments using different structure representations, different attention modules, and different losses. The experimental results demonstrate that, compared to the traditional chemical bond structure representations, the rotation and translation invariant structure representations proposed in this work can improve the SCC prediction accuracy;with the graph embedded local self-attention, the mean absolute error (MAE) of the prediction model in the validation set decreases from 0.1603 Hz to 0.1067 Hz;using the classification based loss function instead of the scaled regression loss, the MAE of the predicted SCC can be decreased to 0.0963 HZ, which is close to the quantum chemistry standard on CHAMPS dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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AutoLDP: An accurate and efficient artificial intelligence-based tool for automatic labeling of digital pathology

EngMedicine

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EngMedicine 2025年第1期2卷

作者： Yingnan Zhao Huifen Ye Jing Yang Su Yao Maohua Lv Zhihong Chen Yunrui Ye Qingru Hu Cheng Lu Zaiyi Liu Ke Zhao Zhihua Chen Institute of Computing Science and Technology Guangzhou University Guangzhou 510006 China Huadu District Maternal and Child Health Care Hospital Guangzhou 510800 China Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China Department of Radiology Fudan University Shanghai Cancer Center Shanghai 200032 China Department of Oncology Shanghai Medical College Fudan University Shanghai 200032 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangzhou 510080 China Department of Gastroenterology The Second Affiliated Hospital Guangzhou Medical University Guangzhou 510260 China Department of Pathology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China Medical Research Institute Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China

Background Whole-slide image (WSI) is foundational for artificial intelligence in tumor diagnosis, treatment planning, and prognosis prediction. Efficient management of WSI labels is crucial for clinical digitalization; however, manual or semiautomatic methods limit scalability. Enhancing automatic pathological label recognition is critical to advancing digital pathology, improving efficiency, and drive precision oncology. Methods We developed Auto LDP, a method for automatic labeling of digital pathology, which identifies textual information used for labeling slides. The method includes four steps: identifying text position using the CRAFT model, recognizing text content using the ParSeq model, identifying slice type using the ConvNext classifier, and combining relevant information to generate a new name. The naming format is divided into four parts: pathology ID, wax block ID, staining type, and slice type. We used the accuracy and processing time to validate our method using two validation sets. Results The AutoLDP system was 20 times faster than manual labeling. The files per minute in the solid-state drives of CRAFT + ParSeq were the highest among all methods at 136.95 in validation set 1 and 170.95 in validation set 2. We compared the proposed model with several commonly used text detection and recognition models including ABinet, CRNN, TRBA, and Vitstr. The results show that we achieved an accuracy of 97.60 % in just 87.62 s in validation set 1 with 200 cases, which was significantly better than that of the other models. In addition, the accuracy reached 96.98 % in validation set 2 with 13,667 cases, confirming the generalization ability of the model. Conclusion In this study, we proposed a new model, AutoLDP, automates the extraction and recognition of key information from WSI, enabling standardized naming, and significantly improving labeling efficiency. This innovation supports the digital transformation of pathology and advances precision medicine.

关键词： Digital pathology Whole slide image AutoLDP Precision oncology Deep learning

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Megavoltage Computed Tomography (MVCT) Imaging Quality Improvement via Convolutional Neural Network

Megavoltage Computed Tomography (MVCT) Imaging Quality Impro...

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International Conference on image, Vision and intelligent Systems, ICIVIS 2021

作者： Zhao, Zengjing Dong, Jiwen Niu, Sijie Zhang, Yan Zhu, Jian School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China Department of Radiation Oncology Physics and Technology Shandong Cancer Hospital affiliated to Shandong First Medical University Jinan250022 China

ISBN: (纸本)9789811669620

Clinical application of Megavoltage Computed Tomography (MVCT) is limited to three-dimensional visualization and position of the patient. The noise and low soft tissue contrast of MVCT make it difficult to offer accurate target volume delineation for adaptive radiotherapy and accurate image registration for image-guided radiotherapy, resulting in the less clinical application prospect of MVCT. In this work, we developed a deep learning-based approach to improve the MVCT image quality, so as to learn a mapping between MVCT images and paired planning kilovoltage CT (KVCT) images. The root mean squared error (RMSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) were used to quantify. Specifically, the PSNR and SSIM of the processed images improved to 27.94 ± 1.64 dB and 0.93 ± 0.01 compared with 24.67 ± 1.16 dB and 0.88 ± 0.02 in the original MVCT images, and the RMSE decreased from 23.5 ± 3.14HU to 16.34 ± 3.39HU. The processed images were obtained using the proposed method with less noise and higher soft tissue contrast in comparison with machine learning-based method. The processed MVCT increases the feasibility of depicting the anatomical delineation of tumor and organs at risk, and therefore enables the promising application of image-guided and adaptive helical TomoTherapy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Ventricular Wave Feature Extraction of ECG Signal based on Synthesized Algorithm

Ventricular Wave Feature Extraction of ECG Signal based on S...

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2015 Global Conference on Biological Engineering and Biomedical(CBEB 2015)

作者： Wang Cong Wu Xiaomei Department of Electric Engineering School of InformationFudan University Digital Medical Research Center of Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

Electrocardiogram(ECG) is a record of the electrical activity of the heart. Using computer to extract feature points of ventricular wave of ECG automatically is of great significance, for it can indicate plenty of heart diseases. In this paper,a synthesized algorithm is proposed to analyse ventricular wave of ECG. After multiresolution wavelet decomposition,QRS complex and T wave are reconstructed with different coefficients based on the spectrum characteristics of different portion of ECG. Self-adaptive threshold is applied in peak detection. Maximum and minimum modulus is used to obtain QRS onset and offset. Area integration is adopted to acquire T offset. This synthesized algorithm achieves a preferable detection result in QT database of Physionet with higher accuracy and precision, which will lay a better foundation for analysis and prediction of ventricular heart diseases.

关键词： ventricular wave of ECG signal analysis,feature extraction,synthesized algorithm

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An accelerative method for multimodality medical image registration based on CUDA

An accelerative method for multimodality medical image regis...

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Congress on image and Signal Processing, CISP

作者： Jinzhu Yang Fangfang Han Chaolu Fen dazhe Zhao Yanfei Wang Key Laboratory of Medical Image Computing of Ministry of Education Northeast University Shenyang China MR Division Liaoning Kampo Medical System Co. Ltd Shenyang China

image registration algorithm based on normal mutual information (NMI) has been adopted in multimodality medical image registration research for a few years. Basing on the whole information of the image pixels, this method has no incertitude of the problems about the similar parts, different range of values, segmentation, boundary, and so on. The results have always been more satisfied by doctors than other registration methods. However, medical images usually contain a huge amount of data and the calculation of mutual information takes a lot of statistical analyses. Therefore the operation time is too long to apply in clinical. According to this problem, researchers have intended to improve the algorithm on two aspects, namely software and hardware. Compute Unified Device Architecture (CUDA) is a new technology of parallel computing with broad-ranging application on image processing, reconstruction, analysis, and much more. Above all, we proposed a new improvement CUDA based method to accelerate NMI registration algorithm. Experiments show it cannot only ensure to provide a correct registration images but also an efficiency improvement as well. And the total speedup ratio nearly reaches 10.

关键词： Positron emission tomography Graphics processing unit Computed tomography image resolution image registration Acceleration Mutual information

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BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neural Network for the Segmentation of Breast Ultrasound images

BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neu...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Zhang Xuanya Li Kai Hu Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Baidu Inc. Beijing China Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Xiangnan University Chenzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China

ISBN: (纸本)9781665429825

In this paper, we propose a novel convolutional neural network based on boundary-guided and region-aware (BGRA-Net) for breast tumor segmentation in ultrasound images. In particular, in the encoding stage, we propose a boundary-guided module (BGM) to guide the learning of boundary features in the decoding stage by explicitly strengthening the extracted boundary information. Meanwhile, in the decoding stage, we propose a region-aware module (RAM) to integrate different levels of detailed and semantic features to improve the comprehensive representation of tumor regional features. Besides, a scale-adaptive module (SAM) is further proposed to capture the characteristics of tumors with different sizes between the encoding and decoding stages. To evaluate the effectiveness of our BGRA-Net, we conduct extensive experiments on the UDIAT dataset and compare it with eight state-of-the-art methods. The experimental results show that our BGRA-Net outperforms the state-of-the-art methods and can achieve accurate segmentation of breast tumors with ambiguous boundaries.

关键词： Deep learning image segmentation Breast tumors Ultrasonic imaging Semantics Feature extraction Visual effects

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Dynamic Neural Radiance Field From Defocused Monocular Video

arXiv

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arXiv 2024年

作者： Luo, Xianrui Sun, Huiqiang Peng, Juewen Cao, Zhiguo Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of AIA Huazhong University of Science and Technology China College of Computing and Data Science Nanyang Technological University Singapore

Dynamic Neural Radiance Field (NeRF) from monocular videos has recently been explored for space-time novel view synthesis and achieved excellent results. However, defocus blur caused by depth variation often occurs in video capture, compromising the quality of dynamic reconstruction because the lack of sharp details interferes with modeling temporal consistency between input views. To tackle this issue, we propose D2RF, the first dynamic NeRF method designed to restore sharp novel views from defocused monocular videos. We introduce layered Depth-of-Field (DoF) volume rendering to model the defocus blur and reconstruct a sharp NeRF supervised by defocused views. The blur model is inspired by the connection between DoF rendering and volume rendering. The opacity in volume rendering aligns with the layer visibility in DoF rendering. To execute the blurring, we modify the layered blur kernel to the ray-based kernel and employ an optimized sparse kernel to gather the input rays efficiently and render the optimized rays with our layered DoF volume rendering. We synthesize a dataset with defocused dynamic scenes for our task, and extensive experiments on our dataset show that our method outperforms existing approaches in synthesizing all-in-focus novel views from defocus blur while maintaining spatial-temporal consistency in the scene. Copyright © 2024, The Authors. All rights reserved.

关键词： Volume rendering

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Arbitrary shape text detection method based on improved PSE network structure

Arbitrary shape text detection method based on improved PSE ...

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IEEE International Conference of Safety Produce Informatization (IICSPI)

作者： Chen Xie Guangxi Chen Min Tang Qiaochu Li Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728177380

ISBN: (纸本)9781728177397

The problem of low efficiency of text detection in natural scenes due to the Curved text or the multi-directional text, a arbitrary shape text detection method based on PSE network structure is proposed. By changing the backbone network of the original PSE and adopting a new lightweight network as backbone, and the post-processing method of the original network is optimized, which effectively improves the speed without losing the original accuracy. The experimental results show that the speed of the proposed method is 5.81 FPS/s compared with the original PSE on CTW1500 data set. The improved network has good robustness and generalization ability, and has better overall performance.

关键词： Shape Conferences Production Robustness

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Adaptive Mix for Semi-Supervised medical image Segmentation

arXiv

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arXiv 2024年

作者： Shen, Zhiqiang Cao, Peng Su, Junming Yang, Jinzhu Zaiane, Osmar R. School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, generating strong-perturbed samples for strong-weak pseudo-supervision. Existing mix-up operations are performed either randomly or with predefined rules, such as replacing low-confidence patches with high-confidence ones. The former lacks control over the perturbation degree, leading to overfitting on randomly perturbed samples, while the latter tends to generate images with trivial perturbations, both of which limit the effectiveness of consistency learning. This paper aims to answer the following question: How can image mix-up perturbation be adaptively performed during training? To this end, we propose an Adaptive Mix algorithm (AdaMix) for image mix-up in a self-paced learning manner. Given that, in general, a model's performance gradually improves during training, AdaMix is equipped with a self-paced curriculum that, in the initial training stage, provides relatively simple perturbed samples and then gradually increases the difficulty of perturbed images by adaptively controlling the perturbation degree based on the model's learning state estimated by a self-paced regularize. We develop three frameworks with our AdaMix, i.e., AdaMix-ST, AdaMix-MT, and AdaMix-CT, for semi-supervised medical image segmentation. Extensive experiments on three public datasets, including both 2D and 3D modalities, show that the proposed frameworks are capable of achieving superior performance. For example, compared with the state-of-the-art, AdaMix-CT achieves relative improvements of 2.62% in Dice similarity coefficient and 48.25% in average surface distance on the ACDC dataset with 10% labeled data. The results demonstrate that mix-up operations with dynamically adjusted perturbation strength based on the segmentation model's state can significantly enhance the effectiveness of consistency regularization. The code will be released at https://***/Senyh/AdaMix. Copyright © 2024,

关键词： Self-supervised learning

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Multi-Adapter RGBT Tracking

Multi-Adapter RGBT Tracking

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Cheng Long Li Andong Lu Ai Hua Zheng Zhengzheng Tu Jin Tang Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-of-the-art RGB and RGBT tracking algorithms.

关键词： Adaptation models Robustness Target tracking Feature extraction Visualization Task analysis

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