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Convolutional neural network-based program to predict lymph node metastasis of non-small cell lung cancer using 18F-FDG PET
PurposeTo develop a convolutional neural network (CNN)-based program to analyze maximum intensity projection (MIP) images of 2-deoxy-2-[F-18]fluoro- d ...
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Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging
BackgroundUpon the discovery of ovarian cysts, obstetricians, gynecologists, and ultrasound examiners must address the common clinical challenge of...
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Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network
Lung cancer is one of the leading causes of death worldwide and early detection is crucial to reduce the mortality. A reliable computer-aided...
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TransMVAN: Multi-view Aggregation Network with Transformer for Pneumonia Diagnosis
Automated and accurate classification of pneumonia plays a crucial role in improving the performance of computer-aided diagnosis systems for chest...
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Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images
BackgroundSkin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and...
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TriConvUNeXt: A Pure CNN-Based Lightweight Symmetrical Network for Biomedical Image Segmentation
Biomedical image segmentation is essential in clinical practices, offering critical insights for accurate diagnosis and strategic treatment...
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Development and Preliminary Validation of a Novel Convolutional Neural Network Model for Predicting Treatment Response in Patients with Unresectable Hepatocellular Carcinoma Receiving Hepatic Arterial Infusion Chemotherapy
The goal of this study was to evaluate the performance of a convolutional neural network (CNN) with preoperative MRI and clinical factors in...
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Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography
Recent advances in contrastive learning have significantly improved the performance of deep learning models. In contrastive learning of medical...
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A preliminary study on the application of deep learning methods based on convolutional network to the pathological diagnosis of PJI
ObjectiveThis study aimed to establish a deep learning method based on convolutional networks for the preliminary study of the pathological diagnosis...
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CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network
BackgroundAt present, numerous challenges exist in the diagnosis of pancreatic SCNs and MCNs. After the emergence of artificial intelligence (AI),...
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Classification of Pulmonary Nodules in 2-[18F]FDG PET/CT Images with a 3D Convolutional Neural Network
Purpose2-[ 18 F]FDG PET/CT plays an important role in the management of pulmonary nodules. Convolutional neural networks (CNNs) automatically learn...
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Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma
ObjectiveThis study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low...
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A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet
PurposeTo improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the...
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Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network
PurposeThe goal of this work is to demonstrate the feasibility of directly generating attenuation-corrected PET images from non-attenuation-corrected...
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Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network
PurposeA skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding...
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Optimizing a Deep Residual Neural Network with Genetic Algorithm for Acute Lymphoblastic Leukemia Classification
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer worldwide, and it is characterized by the production of immature malignant...
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Assessing Endoscopic Response in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy: Development and Validation of a Highly Accurate Convolutional Neural Network
BackgroundRectal tumors display varying degrees of response to total neoadjuvant therapy (TNT). We evaluated the performance of a convolutional...
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Feature Aggregation and Refinement Network for 2D Anatomical Landmark Detection
Localization of anatomical landmarks is essential for clinical diagnosis, treatment planning, and research. This paper proposes a novel deep network...
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Effect of Training Data Volume on Performance of Convolutional Neural Network Pneumothorax Classifiers
Large datasets with high-quality labels required to train deep neural networks are challenging to obtain in the radiology domain. This work...
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Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network
In the forensic estimation of bone age, the pelvis is important for identifying the bone age of teenagers. However, studies on this topic remain...
