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An early detection and segmentation of Brain Tumor using Deep Neural Network
BackgroundMagnetic resonance image (MRI) brain tumor segmentation is crucial and important in the medical field, which can help in diagnosis and...
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Evaluation of Endoscopic Response Using Deep Neural Network in Esophageal Cancer Patients Who Received Neoadjuvant Chemotherapy
BackgroundWe previously reported that endoscopic response evaluation can preoperatively predict the prognosis and distribution of residual tumors...
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Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies
PurposeDelay in diagnosis can contribute to poor outcomes in pancreatic ductal adenocarcinoma (PDAC), and new tools for early detection are required....
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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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Refined Residual Deep Convolutional Network for Skin Lesion Classification
Skin cancer is the most common type of cancer that affects humans and is usually diagnosed by initial clinical screening, which is followed by...
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Development and multicenter validation of deep convolutional neural network–based detection of colorectal cancer on abdominal CT
ObjectivesThis study aims to develop computer-aided detection (CAD) for colorectal cancer (CRC) using abdominal CT based on a deep convolutional...
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Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis
BackgroundLong-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to...
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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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Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network
Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network,...
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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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Endoscopic Evaluation of Pathological Complete Response Using Deep Neural Network in Esophageal Cancer Patients Who Received Neoadjuvant Chemotherapy—Multicenter Retrospective Study from Four Japanese Esophageal Centers
BackgroundDetecting pathological complete response (pCR) before surgery would facilitate nonsurgical approach after neoadjuvant chemotherapy (NAC)....
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Scale-aware dense residual retinal vessel segmentation network with multi-output weighted loss
BackgroundRetinal vessel segmentation provides an important basis for determining the geometric characteristics of retinal vessels and the diagnosis...
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Estimating critical values from electrocardiogram using a deep ordinal convolutional neural network
BackgroundCritical values are commonly used in clinical laboratory tests to define health-related conditions of varying degrees. Knowing the values,...
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Lymph node detection in CT scans using modified U-Net with residual learning and 3D deep network
PurposeLymph node (LN) detection is a crucial step that complements the diagnosis and treatments involved during cancer investigations. However, the...
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Identification of robust deep neural network models of longitudinal clinical measurements
Deep learning (DL) from electronic health records holds promise for disease prediction, but systematic methods for learning from simulated...
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Multi-scale Bottleneck Residual Network for Retinal Vessel Segmentation
Precise segmentation of retinal vessels is crucial for the prevention and diagnosis of ophthalmic diseases. In recent years, deep learning has shown...
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A generative adversarial neural network with multi-attention feature extraction for fundus lesion segmentation
PurposeFundus lesion segmentation determines the location and size of diabetes retinopathy in fundus image, which assists doctors in develo** the...
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Breast Tumor Classification in Ultrasound Images by Fusion of Deep Convolutional Neural Network and Shallow LBP Feature
Breast cancer is one of the most dangerous and common cancers in women which leads to a major research topic in medical science. To assist physicians...
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Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks
ObjectiveWe propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions.
Methods ...
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Surgical smoke removal via residual Swin transformer network
PurposeIn robot-assisted minimally invasive surgery (RMIS), smoke produced by laser ablation and cauterization causes degradation in the visual...
