Article
Radiology, Nuclear Medicine & Medical Imaging
Jingwen Chen, Rong Cao, Shengyin Jiao, Yunpeng Dong, Zilong Wang, Hua Zhu, Qian Luo, Lei Zhang, Han Wang, Xiaorui Yin
Summary: This study assessed the value of a CAD system for detecting lung nodules on chest CT images. The CAD system demonstrated higher sensitivity compared to manual detection by radiologists, with only a slight increase in false positive rate.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2023)
Review
Computer Science, Information Systems
K. K. Anilkumar, V. J. Manoj, T. M. Sagi
Summary: Leukemia is a non-tumor type of cancer and early diagnosis is crucial. This study categorizes the related works on computer aided diagnosis of leukemia into Machine Learning (ML) and Deep Learning (DL) based technologies. The review found that SVM was widely used in ML based works and CNN dominated the DL category. There is a lack of works on chronic leukemia in both ML and DL categories. The study highlights the need for public datasets and new diagnostic methods for different types of leukemia, particularly chronic leukemia. The shift towards DL based studies for leukemia diagnosis is evident from 2019 onwards and there is a scarcity of reviews classifying the related works into ML and DL techniques.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Engineering, Biomedical
Yuqin Min, Liangyun Hu, Long Wei, Shengdong Nie
Summary: This study systematically reviews the latest techniques in deep learning-based computer-aided detection (CADe) of pulmonary nodules. The results show that deep learning methods significantly transform the detection of pulmonary nodules, and optimizing candidate nodule generation and false positive reduction can achieve optimal results. This study has important implications for guiding future research directions.
PHYSICS IN MEDICINE AND BIOLOGY
(2022)
Article
Automation & Control Systems
Rudong Jing, Wei Zhang, Yanyan Liu, Wenlin Li, Yuming Li, Changsong Liu
Summary: This study proposes a method called YOLOFs to improve the precision of small object detection and address the impact of object instance scale variation. By introducing three key modules, the receptive field of the network is expanded, greatly improving small object detection precision.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Computer Science, Artificial Intelligence
Minmin Zeng, Zhenlei Yan, Shuai Liu, Yanheng Zhou, Lixin Qiu
Summary: A novel approach using a cascaded three-stage convolutional neural networks to automatically predict cephalometric landmarks is proposed in this study, and experimental results demonstrate its competitive performance.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Chemistry, Analytical
Chan-Il Kim, Seok-Min Hwang, Eun-Bin Park, Chang-Hee Won, Jong-Ha Lee
Summary: The study proposed a computer-aided diagnostic algorithm for the classification of malignant melanoma and benign skin tumors using deep learning techniques. By employing U-Net model and convolutional neural networks, the algorithm achieved high accuracy in skin lesion classification.
Article
Computer Science, Interdisciplinary Applications
Kaidi Liu, Zijian Zhao, Pan Shi, Feng Li, He Song
Summary: Surgical tool detection is important for computer-assisted surgery, but data shortage and the balance between accuracy and speed remain challenges. This study manually annotated a new dataset and proposed an enhanced feature-fusion network (EFFNet) for real-time surgical tool detection. The method achieved high accuracy and met the real-time standard.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yuhan Wang, Hak Keung Lam, Zeng-Guang Hou, Rui-Qi Li, Xiao-Liang Xie, Shi-Qi Liu
Summary: To reduce the workload of clinicians and the occurrence of malpositioned catheters, we propose an automatic catheter tip detection framework based on a CNN. The framework includes a modified HRNet module, a segmentation supervision module, and a deconvolution module, which can accurately detect the catheter tip position in X-ray images. Experimental results show that the proposed algorithm outperforms three comparative methods with a mean Pixel Error of 4.11.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Sunyi Zheng, Ludo J. Cornelissen, Xiaonan Cui, Xueping Jing, Raymond N. J. Veldhuis, Matthijs Oudkerk, Peter M. A. van Ooijen
Summary: The study aims to develop an accurate deep learning framework for nodule detection through multiplanar candidate detection and multiscale false positive reduction. The proposed system achieves good performance for lung nodule detection on the LIDC-IDRI dataset, demonstrating its effectiveness across different nodule sizes.
Article
Computer Science, Information Systems
Fernando Roberto Pereira, Joao Mario Clementin De Andrade, Dante Luiz Escuissato, Lucas Ferrari De Oliveira
Summary: A CADe system utilizing DCNN was proposed to improve lung nodule detection performance, achieving a sensitivity of 94.90% and 1.0 False Positives per scan in the LUNA16 challenge. The system combines Mask R-CNN and CT attenuation patterns for accurate 2D bounding box detection and 3D nodule classification, outperforming other existing CADe systems.
Article
Engineering, Multidisciplinary
Jing Lu, Yan Wu, Yao Xiong, Yapeng Zhou, Ziliang Zhao, Liutong Shang
Summary: This paper designs a new computer-aided detection system for breast tumors using three networks and explores the impact of secondary migration on experimental results. The results show that the system built based on VGG16 and ResNet50 performs well, and migration can improve system performance.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Dandan Zhao, Yang Liu, Hongpeng Yin, Zhiqiang Wang
Summary: Automatic pulmonary nodule detection is crucial for early diagnosis and treatment of lung cancer. However, the different types, shapes, and sizes of nodules, especially the diameter of lung nodules (ranging from 3 mm to 30 mm), lead to high false positive rates in detection results, significantly impacting the performance. This paper proposes an adaptive and attentive 3D Convolutional Neural Network (CNN) for automatic pulmonary nodule detection, consisting of candidate nodule detection and false positive reduction. The proposed method effectively increases sensitivity and decreases false positive rates for automated pulmonary nodule detection, as demonstrated by experiments on the LUNA16 dataset.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Gioia Fischer, Alexandra De Silvestro, Mathias Mueller, Thomas Frauenfelder, Katharina Martini
Summary: The computer-aided detection (CAD) system shows higher sensitivity in detecting infectious consolidation and pulmonary nodules on chest X-ray (CXR) images. There are no significant differences in the detection of other pathologies compared to radiologists. The interobserver agreement among radiologists is moderate.
ACADEMIC RADIOLOGY
(2022)
Article
Public, Environmental & Occupational Health
Hao Wang, Na Tang, Chao Zhang, Ye Hao, Xiangfeng Meng, Jiage Li
Summary: This study aimed to implement a standardized protocol for testing the performance of computer-aided detection (CAD) algorithms for pulmonary nodules. The study established a test dataset and applied three specific rules to match algorithm output with a reference standard. The results showed that algorithms performed differently on different types of pulmonary nodules. This centralized testing protocol supports algorithm comparison and performance evaluation.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Computer Science, Information Systems
Hassan Mkindu, Longwen Wu, Yaqin Zhao
Summary: Malignant lung nodules are the worse stage for lung cancer patients, early detection is essential for treatment. This study presents a 3D U-shaped encoding and decoding CNN integrated with channel attention mechanisms for lung nodule detection in chest CT images.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Chemistry, Organic
Zhentao Pan, Xuancheng Yang, Bo Chen, Shuaijun Shi, Tong Liu, Xuqiong Xiao, Linlin Shen, Li Lou, Yongmin Ma
Summary: We report the first example of a visible-light-induced approach for the synthesis of spiroquinazolin(thi)ones, which features an eco-friendly energy source and solvent, metal-free catalysts, step- and atom- economy, a relay catalysis strategy, air as a green oxidant, mild conditions, and easily accessible starting materials.
JOURNAL OF ORGANIC CHEMISTRY
(2022)
Article
Computer Science, Artificial Intelligence
Yating Huang, Xuechen Li, Siting Zheng, Zhongliang Li, Sihan Li, Linlin Shen, Changen Zhou, Zhihui Lai
Summary: The size and shape of the tongue can reflect different pathological changes of the human body in Traditional Chinese Medicine (TCM). In this work, an efficient deep network, TSCWNet, is proposed for tongue size and shape classification. Experimental results demonstrate that the network achieves better classification performance for tongue diagnosis.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Ziqi Jin, Jinheng Xie, Bizhu Wu, Linlin Shen
Summary: In this paper, a weakly supervised pedestrian segmentation framework is proposed to directly generate the foreground mask from person re-identification datasets with only image-level subject ID labels. The Image Synthesis Augmentation (ISA) technique is also introduced to further enhance the dataset. Experimental results demonstrate that the proposed framework learns robust and discriminative features, achieving significant improvement in mAP compared to the baseline on widely used datasets including Market-1501, CUHK03, and MSMT17. The code will be made available soon.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Syed Furqan Qadri, Hongxiang Lin, Linlin Shen, Mubashir Ahmad, Salman Qadri, Salabat Khan, Maqbool Khan, Syeda Shamaila Zareen, Muhammad Azeem Akbar, Md Belal Bin Heyat, Saqib Qamar
Summary: This study proposes a patch-based deep learning approach for automatic CT vertebral segmentation. The method extracts discriminative features from unlabeled data using a stacked sparse autoencoder and achieves accurate segmentation of CT vertebrae.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhongliang Li, Xuechen Li, Zhihao Jin, Linlin Shen
Summary: In this paper, a novel self-supervised pretraining method based on pseudo-lesion generation and restoration was proposed for COVID-19 diagnosis. The method trained an encoder-decoder architecture-based U-Net using pairs of pseudo-COVID-19 images and normal CT images for image restoration, and then fine-tuned the pretrained encoder using labeled data. Experimental results demonstrated that the proposed method extracted better feature representation for COVID-19 diagnosis, achieving higher accuracy compared to the supervised model pretrained on large-scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jiajun Wen, Honglin Chu, Zhihui Lai, Tianyang Xu, Linlin Shen
Summary: This paper proposes an innovative method called EFSCF, which performs jointly sparse feature learning to handle the spatial boundary effect effectively while suppressing the influence of background pixels and noises. The proposed method achieves better tracking performance than the state-of-the-art trackers by exploring the structural sparsity in rows and columns of a learned filter simultaneously.
Article
Neurosciences
Xiuzhi Zhao, Wenting Chen, Weicheng Xie, Linlin Shen
Summary: This study proposes a Style Attention based Global-local Aware GAN to generate personalized caricatures. It integrates the facial characteristics of a subject through a landmark-based warp controller for personalized shape exaggeration and uses a style-attention module for appropriate fusion of facial features and caricature style. The results indicate that the proposed method can preserve the identity of input photos and generate caricatures close to those drawn by real artists.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Plant Sciences
Linlin Shen, Haiyan Deng, Ganglong Zhang, Anqi Ma, Xiaoyong Mo
Summary: Climate warming poses a significant threat to global ecosystems, impacting the geographic distribution and suitable growth areas of species. This study focused on predicting the potential cultivation regions of Castanopsis hystrix Miq., a research object, based on the MaxEnt model and environmental variables. The key factors affecting the distribution area of C. hystrix Miq. were identified as the minimum temperature of the coldest month, precipitation of the driest month, and precipitation of the warmest quarter. The suitable cultivation regions were found in central and southern China, with a range of 18-34°N and 89-122°E, covering an area of 261.95 x 10(4) km(2). Under different climate scenarios, the spatial pattern of C. hystrix Miq. will migrate to different regions, with varying changes in suitable area and cultivation distribution.
Article
Computer Science, Artificial Intelligence
Ya-Nan Zhang, Linlin Shen, Zhihui Lai
Summary: In the field of computer vision, removing rain streaks from images is an important task as it affects the performance and quality of subsequent tasks and outdoor images. Deep learning-based methods have been proposed to address this issue, but they lack interpretability and have limited performance in detail restoration and rain streak removal. This paper introduces a rain streaks model-driven deep network, MSANet, to overcome these limitations.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Junhong Zhang, Zhihui Lai, Heng Kong, Linlin Shen
Summary: In this paper, a new robust manifold twin bounded SVM (RMTBSVM) algorithm is proposed, which considers both robustness and discriminability. By using the capped L-1-norm as the distance metric and adding robust manifold regularization, the robustness and classification performance are improved. The algorithm is extended for nonlinear classification using the kernel method.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Siyang Song, Shashank Jaiswal, Enrique Sanchez, Georgios Tzimiropoulos, Linlin Shen, Michel Valstar
Summary: This article addresses two important issues in automatic personality analysis systems: the use of short video segments or single frames for inferring personality traits, and the lack of methods for encoding person-specific facial dynamics. To tackle these issues, the paper proposes a novel Rank Loss for self-supervised learning of facial dynamics and a method to represent person-specific dynamics. The approach achieves promising results in personality estimation and shows the importance of the tasks performed by the subject in the video and the use of multi-scale dynamics.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Thermodynamics
Lili Liu, Cai Chen, Linlin Shen, Gang Xu, Yufeng Wen, Xianshi Zing
Summary: The pressure dependence of lattice and elastic constants of g-TiAl, DO22-Al3Ti, and alpha(2)-Ti3Al binary precipitates was investigated using a first-principles approach. The calculated results at 0 GPa and 0 K were in good agreement with existing experimental and theoretical values. The temperature and pressure dependencies of bulk modulus, Gibbs free energy, thermal expansion coefficient, and heat capacity at constant pressure were systematically studied using density-functional perturbation theory (DFPT) under the quasiharmonic approximation (QHA) in the ranges of 0-1000 K and 0-30 GPa.
HIGH TEMPERATURES-HIGH PRESSURES
(2023)
Article
Computer Science, Artificial Intelligence
Yang Zhang, Linlin Shen
Summary: In this study, an automatic learning rate tuning method for memristive deep learning systems is presented. The method utilizes memristors to adjust the adaptive learning rate in deep neural networks. The proposed method is robust to noisy gradients, various architectures, and different datasets and can address the issue of over-fitting. Moreover, a quantized neural network architecture is utilized in the presented system, leading to an increase in training efficiency without the loss of testing accuracy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhe Kong, Wentian Zhang, Feng Liu, Wenhan Luo, Haozhe Liu, Linlin Shen, Raghavendra Ramachandra
Summary: This study empirically proves the importance of model initialization for generalization and proposes a self-supervised learning-based method to address the issue of unknown PAI.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Jinheng Xie, Xianxu Hou, Kai Ye, Linlin Shen
Summary: This paper proposes a novel Cross Language Image Matching (CLIMS) framework for Weakly Supervised Semantic Segmentation (WSSS) based on the Contrastive Language-Image Pre-training (CLIP) model. By introducing natural language supervision and designing matching losses, CLIMS can activate more complete object regions and suppress related background regions. Experimental results show that CLIMS outperforms previous state-of-the-art methods.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Article
Computer Science, Artificial Intelligence
Qianghua Liu, Yu Tian, Tianshu Zhou, Kewei Lyu, Ran Xin, Yong Shang, Ying Liu, Jingjing Ren, Jingsong Li
Summary: This study proposes a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML). It significantly improves the diagnostic process in primary health care and helps general practitioners diagnose few-shot diseases more accurately.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Balazs Borsos, Corinne G. Allaart, Aart van Halteren
Summary: The study demonstrates the feasibility of predicting functional outcomes for ischemic stroke patients and the usability of multimodal deep learning architectures for this purpose.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Abdelmoniem Helmy, Radwa Nassar, Nagy Ramdan
Summary: This study utilizes machine learning models to detect depression symptoms in Arabic and English texts, and provides manually and automatically annotated tweet corpora. The study also develops an application that can detect tweets with depression symptoms and predict depression trends.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)