MMSeg: Multi-scale Vision Mamba for Lightweight Generalizable Medical Image Segmentation [0.03%]
MMSeg: 轻量级通用医学图像分割的多尺度视觉蟒蛇模型
Yayuan Mo,Yunhao Chen,Kaiwen Zhu et al.
Yayuan Mo et al.
Medical image segmentation plays a crucial role in computer-aided diagnosis and treatment planning, yet existing methods suffer from limited cross-domain generalization and excessive computational complexity. Despite the recent Segment Anyt...
Self-Supervised Endoscopic Depth Estimation via Deep Feature-Aware Reconstruction and Dual-Path Feature Aggregation Pyramid [0.03%]
自监督内窥镜深度估计的深层特征感知重建与双路径特征聚合金字塔方法
Yukang Ren,Yanping Chen
Yukang Ren
As a core tool for minimally invasive diagnosis and treatment of gastrointestinal, respiratory, and urinary diseases, endoscopes are now used in increasingly precise surgeries. AI-assisted endoscopic sequence depth estimation has thus becom...
Automated Measurement of Midpalatal Suture Density Ratio Based on Deep Learning [0.03%]
基于深度学习的腭中缝密度比自动测量方法
Zhouxin Zhou,Qi Li,Shuxi Xu et al.
Zhouxin Zhou et al.
Rapid maxillary expansion (RME) is a common method for maxillary transverse deficiency. While the midpalatal suture density (MPSD) ratio is a critical predictor of skeletal responsiveness to RME, its manual measurement on cone-beam computed...
A Native Strategy for Integrating Deep-Learning Models for Segmentation into a Radiological Viewer [0.03%]
一种用于放射学图像分析软件中集成深度学习分割模型的本地策略
Pau Xiberta,Marc Ruiz,Màrius Vila et al.
Pau Xiberta et al.
The use of deep-learning (DL) models to support and automate medical imaging diagnostic procedures has become an ongoing focus of research and development. Despite advances in the subject, the integration of such solutions into clinical dia...
BDU-Net: An Edge-Segmentation-Oriented U-Shaped Network for Pediatric Knee Joint Segmentation [0.03%]
基于边缘的儿科膝关节分割U型网络BDU-Net
Huazheng Zhu,Yaping Liu,Zhuo Cheng et al.
Huazheng Zhu et al.
The growth plate and articular cartilage are essential for children's bone development. Precise segmentation of cartilage in MRI images enables the extraction of quantitative indicators for health assessment and risk identification. Therefo...
Optimal Lossy Compression Scheme of 3D Volumetric Ultrasound Images For Remote Breast Cancer Screening [0.03%]
乳腺癌筛查的3D体积超声图像的最优有损压缩方案
Zengan Huang,Jun Huang,Xiao Li et al.
Zengan Huang et al.
While lossy compression is gaining acceptance in medical imaging research, its practical use remains limited due to the lack of specific implementation guidelines. This study investigates the feasibility and diagnostic reliability of variou...
An Effective Breast Cancer Classification System Using Multiple Feature Extraction Techniques with Multi-scale Attention-Based Feature Fusion Model [0.03%]
基于多尺度注意的特征融合模型的乳腺癌分类系统
Atul Kumar Ramotra,Goldi Chandrapal Jarbais
Atul Kumar Ramotra
Breast cancer ranks as the second leading cause of death in women. Early identification of breast cancer can significantly reduce women's mortality rates. Due to the time-consuming nature of manual breast cancer diagnosis, an automated syst...
ITSRS: An Inverse Taylor Series Adaptive Loss Based on Synergized Regional-Structural Information for Medical Image Segmentation [0.03%]
基于协同区域结构信息的自适应损失逆泰勒级数回归法在医学图像分割中的应用
Rui Han,Zhiming Cheng,Jianxiang Zhao et al.
Rui Han et al.
Medical image segmentation is fundamental for accurate diagnosis, treatment planning, and disease monitoring. The design of loss functions plays a central role in advancing medical image segmentation. Due to computing gradients across the e...
Cervical Intraepithelial Neoplasia (CIN1-3) Disease Grading Using a Mixture of Experts Approach [0.03%]
基于专家混合模型的宫颈上皮内瘤变（CIN1-3）分级诊断方法研究
Mohammad Khaleel Sallam Ma&#x;aitah,Abdulkader Helwan,Safa Ghannam et al.
Mohammad Khaleel Sallam Ma&#x;aitah et al.
Accurate grading of cervical intraepithelial neoplasia (CIN1-3) from colposcopic images is clinically critical yet challenging due to subtle inter-grade morphology and substantial imaging variability. We propose an attention-guided mixture-...
Open-Access Fully Automated Intravenous Contrast Detection and Body Part Classification for Computed Tomography Scans: The FALCON Model [0.03%]
开放获取的全自动静脉内对比剂检测和CT扫描身体部位分类：FALCON模型
Julian A Westphal,Philipp Kaess,Lea Mantz et al.
Julian A Westphal et al.
Presence of intravenous contrast on computed tomography (CT) scans is often unreliably documented, especially in large research datasets. FALCON is an open-access fully automated deep learning model enabling large-scale intravenous contrast...