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Article
Computer Science, Artificial Intelligence
Razieh Pourdarbani et al.
Summary: The presence of bruises on fruits indicates cell damage, causing a decrease in the ability of the peel to protect the fruits from oxygen, resulting in the breakdown of cell walls and membranes. This leads to undesirable brown coloration due to chemical reactions. Early detection of bruises prevents low-quality fruits from entering the market. This study aims to identify bruised lemon fruits using 3D-convolutional neural networks (3D-CNN) and hyperspectral imaging. Results showed that the ResNet model achieved the highest classification accuracy (90.47%) among the tested models.
Article
Health Care Sciences & Services
Cong Lin et al.
Summary: This paper proposes a lung disease detection neural network called CXR-RefineDet, which combines the advantages of RepVGG block and Resblock to design a strong feature extraction backbone network RRNet. By introducing an Information Reuse structure, the network's feature utilization is improved. Experimental results show that CXR-RefineDet achieves higher detection accuracy and inference speed compared to two-stage object detection algorithms using powerful backbone networks like ResNet-50 and ResNet-101, providing possibilities for the implementation of computer-aided diagnosis systems.
JOURNAL OF HEALTHCARE ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Hongfeng You et al.
Summary: This paper proposes a simple and efficient dual-rotation network (DR-Net) to enhance the quality of both local and global feature maps for medical image segmentation with small samples. Experimental results show that the DR-Net method achieves higher segmentation accuracy on both the CHAOS and BraTS datasets compared to state-of-the-art methods.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Lei Song et al.
Summary: Medical image registration is a crucial task in computer-aided medical diagnosis, and recent research has focused on using deep learning methods to improve its accuracy. This paper introduces the use of Transformer, a powerful global modeling tool, to enhance medical image registration. By combining Transformer with CNN in a hybrid network, the proposed method is able to extract both local and global information, resulting in improved accuracy in brain MRI scans compared to state-of-the-art approaches.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Manjit Kaur et al.
Summary: The proposed approach utilizes NSCT and Xception for feature extraction and fusion of multi-modality medical images, outperforming competitive approaches in experimental results.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Michael Adebisi Fayemiwo et al.
Summary: The study utilized Deep Transfer Learning Model (DTL) to classify a real-life COVID-19 dataset of chest X-ray images, achieving high accuracy for COVID-19 detection through a classification of binary and three-class scenarios. This approach improved diagnostic accuracy for COVID-19 classification.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Ke Lei et al.
Summary: This article utilizes unpaired adversarial training for reconstruction networks, where a generator suppresses input image artifacts and a discriminator scores reconstruction quality based on the Wasserstein distance. Experimental results demonstrate that unpaired training enables diagnostic-quality reconstruction even in the absence of high-quality image labels or with limited label availability.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Dominik Mueller et al.
Summary: MIScnn is an open-source Python library designed to provide an intuitive API for fast construction of medical image segmentation pipelines, offering high configurability and multiple open interfaces for full pipeline customization. By running cross-validation on the Kidney Tumor Segmentation Challenge 2019 data set, MIScnn demonstrated powerful predictive capabilities based on the standard 3D U-Net model, showing that researchers can quickly set up a complete medical image segmentation pipeline with just a few lines of code.
BMC MEDICAL IMAGING
(2021)
Article
Biology
Sertan Serte et al.
Summary: COVID-19, a new type of pneumonia coronavirus, has caused many infections and deaths worldwide. AI techniques can assist radiologists in quickly and accurately detecting COVID-19 infection on CT scans, improving diagnostic efficiency.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Shuai Wang et al.
Summary: This study utilized artificial intelligence to extract radiological features from CT images of COVID-19, achieving timely and accurate diagnosis of the disease, demonstrating the feasibility of using AI for disease control.
EUROPEAN RADIOLOGY
(2021)
Article
Cell Biology
Axel H. Masquelin et al.
Summary: Utilizing multi-level DWT composition instead of early convolutional layers in a DNN may improve image classification, reduce the number of network parameters requiring evaluation, and result in a substantially faster convergence rate. Multi-level DWT performed better than convolutional layers when utilizing multiple kernel resolutions.
HISTOCHEMISTRY AND CELL BIOLOGY
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Ke Zhu et al.
Summary: CSRA, a class-specific residual attention module, achieves state-of-the-art results in multi-label recognition through a simple approach, showcasing its ease of implementation and computational efficiency.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Article
Medicine, General & Internal
Chaolin Huang et al.
Article
Computer Science, Artificial Intelligence
Grant Haskins et al.
MACHINE VISION AND APPLICATIONS
(2020)
Article
Radiology, Nuclear Medicine & Medical Imaging
Tao Ai et al.
Article
Radiology, Nuclear Medicine & Medical Imaging
Ho Yuen Frank Wong et al.
Article
Computer Science, Artificial Intelligence
Jiuxiang Gu et al.
PATTERN RECOGNITION
(2018)
Article
Radiology, Nuclear Medicine & Medical Imaging
Paras Lakhani et al.
Review
Multidisciplinary Sciences
Yann LeCun et al.
