Harnessing Deep Learning for Accurate Pathological Assessment of Brain Tumor Cell Types [0.03%]
利用深度学习准确评估脑肿瘤细胞类型病理学的方法
Chongxuan Tian,Yue Xi,Yuting Ma et al.
Chongxuan Tian et al.
Primary diffuse central nervous system large B-cell lymphoma (CNS-pDLBCL) and high-grade glioma (HGG) often present similarly, clinically and on imaging, making differentiation challenging. This similarity can complicate pathologists' diagn...
Interactive Multi-scale Fusion: Advancing Brain Tumor Detection Through Trans-IMSM Model [0.03%]
交互式多尺度融合：通过Trans-IMSM模型推进脑肿瘤检测技术
Vasanthi Durairaj,Palani Uthirapathy
Vasanthi Durairaj
Multi-modal medical image (MI) fusion assists in generating collaboration images collecting complement features through the distinct images of several conditions. The images help physicians to diagnose disease accurately. Hence, this resear...
Deep Convolutional Neural Network for Automated Staging of Periodontal Bone Loss Severity on Bite-wing Radiographs: An Eigen-CAM Explainability Mapping Approach [0.03%]
基于咬合片的牙周骨丢失严重程度自动分期的深度卷积神经网络：一种Eigen-CAM可解释性映射方法
Mediha Erturk,Muhammet Üsame Öziç,Melek Tassoker
Mediha Erturk
Periodontal disease is a significant global oral health problem. Radiographic staging is critical in determining periodontitis severity and treatment requirements. This study aims to automatically stage periodontal bone loss using a deep le...
Construction and Validation of a General Medical Image Dataset for Pretraining [0.03%]
通用医疗图像预训练模型的数据集构建与验证方法研究
Rongguo Zhang,Chenhao Pei,Ji Shi et al.
Rongguo Zhang et al.
In the field of deep learning for medical image analysis, training models from scratch are often used and sometimes, transfer learning from pretrained parameters on ImageNet models is also adopted. However, there is no universally accepted ...
EAAC-Net: An Efficient Adaptive Attention and Convolution Fusion Network for Skin Lesion Segmentation [0.03%]
一种有效的自适应注意和卷积融合网络用于皮肤病变分割
Chao Fan,Zhentong Zhu,Bincheng Peng et al.
Chao Fan et al.
Accurate segmentation of skin lesions in dermoscopic images is of key importance for quantitative analysis of melanoma. Although existing medical image segmentation methods significantly improve skin lesion segmentation, they still have lim...
Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study [0.03%]
基于机器学习的多参数MRI影像组学预测直肠癌神经周围浸润的列线图模型的预可行性研究
Yueyan Wang,Aiqi Chen,Kai Wang et al.
Yueyan Wang et al.
This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonance radiomics and clinical risk factors, for forecasting perineural invasion in rectal cance...
Deep Learning-Based Prediction of Post-treatment Survival in Hepatocellular Carcinoma Patients Using Pre-treatment CT Images and Clinical Data [0.03%]
基于深度学习的肝细胞癌患者治疗前CT影像和临床数据的术后生存预测
Kyung Hwa Lee,Jungwook Lee,Gwang Hyeon Choi et al.
Kyung Hwa Lee et al.
The objective of this study was to develop and evaluate a model for predicting post-treatment survival in hepatocellular carcinoma (HCC) patients using their CT images and clinical information, including various treatment information. We co...
Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences [0.03%]
基于深度学习优化急性卒中MRI分割：自配置神经网络仅使用DWI序列即可提供高性能
Peter Kamel,Adway Kanhere,Pranav Kulkarni et al.
Peter Kamel et al.
Segmentation of infarcts is clinically important in ischemic stroke management and prognostication. It is unclear what role the combination of DWI, ADC, and FLAIR MRI sequences provide for deep learning in infarct segmentation. Recent techn...
Ensemble of Deep Learning Architectures with Machine Learning for Pneumonia Classification Using Chest X-rays [0.03%]
基于机器学习的深度学习架构在胸部X光肺炎分类中的应用研究
Rupali Vyas,Deepak Rao Khadatkar
Rupali Vyas
Pneumonia is a severe health concern, particularly for vulnerable groups, needing early and correct classification for optimal treatment. This study addresses the use of deep learning combined with machine learning classifiers (DLxMLCs) for...
The Usefulness of Low-Kiloelectron Volt Virtual Monochromatic Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction Technique in Improving the Delineation of Pancreatic Ductal Adenocarcinoma [0.03%]
基于深度学习图像重建技术的低千电子伏特虚拟单色对比剂增强CT在胰腺导管腺癌边界改善中的应用价值研究
Yasutaka Ichikawa,Yoshinori Kanii,Akio Yamazaki et al.
Yasutaka Ichikawa et al.
To evaluate the usefulness of low-keV multiphasic computed tomography (CT) with deep learning image reconstruction (DLIR) in improving the delineation of pancreatic ductal adenocarcinoma (PDAC) compared to conventional hybrid iterative reco...