Elizabeth A Krupinski,Marly van Assen,Carlo N De Cecco et al.
Elizabeth A Krupinski et al.
The purpose was to examine how variations in AI output design affect radiologists' performance in interpreting chest X-rays. Eight readers interpreted 80 COVID-19 chest images under five AI conditions in this retrospective study: no feedbac...
How is Bias Learned in Medical Image Analysis Models? An Exploration of the Encoding of Demographic Information in Deep Learning Models Trained to Detect Abnormalities on Chest X-Rays [0.03%]
医学影像分析模型中的偏见是如何产生的？深度学习模型中人口统计信息编码的探索：基于胸部X光片异常检测模型
Nikhil Cherian Kurian,Phoenix Williams,Sahar Abdulrahman et al.
Nikhil Cherian Kurian et al.
Deep learning models achieve strong diagnostic performance in medical imaging, yet often exhibit systematic performance disparities across demographic subgroups. Although prior work has shown that attributes such as age, sex and race are en...
AI-Based Opportunistic CT Risk Assessment Using TotalSegmentator in Osteoporotic Vertebral Fractures [0.03%]
基于人工智能的机会主义CT风险评估在骨质疏松性椎体骨折中的应用_totalSegmentator的应用
Magdalena Seng,Jakob Wasserthal,Michael Bach et al.
Magdalena Seng et al.
Osteoporotic vertebral fractures impair quality of life and increase both morbidity and mortality, yet they are largely preventable. Routine CT examinations offer an opportunity for early risk stratification. This study aimed to evaluate wh...
A Computationally Efficient and Improved Brain Tumor Recognition System by MRI-Segmentation Integrated Classification Network [0.03%]
一种计算高效的改进的脑肿瘤识别系统基于MRI分割集成分类网络
Namya Musthafa,Qurban Memon,Mohammad Masud
Namya Musthafa
Brain tumors present a major global health concern, and a precise diagnosis is essential for proper treatment. Many existing MRI-based machine learning approaches focus solely on segmentation or classification, rather than addressing both t...
Ultrasound Domain Adaptation for Robust Kidney Segmentation via Spectral-Similarity-Guided Translation [0.03%]
基于光谱相似引导翻译的肾脏分割域适应方法
De Yu,Jinyan Cai,Menglin Wu et al.
De Yu et al.
Accurate kidney ultrasound segmentation is fundamental for clinical measurement and computer-aided diagnosis. However, domain shifts across devices and centers-manifested as differences in grayscale intensity, contrast, and speckle texture ...
Dynamic Fuzzy-Gaussian Modeling (DynFGM): A Kurtosis-Adaptive Unsupervised Framework for Automated Adipose Tissue Segmentation in Abdominal MRI [0.03%]
动态模糊高斯模型（DynFGM）：一种用于腹部MRI中自动脂肪组织分割的适应峰度的无监督框架
Asefa Adimasu Taddese,Joshua D K Bernal,Chit K Leung et al.
Asefa Adimasu Taddese et al.
Accurate MRI-based quantification of abdominal adipose tissue is critical for metabolic risk assessment but is limited by labor-intensive manual segmentation and the extensive labeled-data dependency of deep learning models. We introduce Dy...
Computerized Classification Method for Glioma Molecular Subtypes on Brain MR Images Using SAM-Med3D with Low-Rank Adaptation [0.03%]
基于SAM-Med3D与低秩自适应的胶质瘤分子亚型脑MRI计算机分类方法
Akiyoshi Hizukuri
Akiyoshi Hizukuri
It is important to identify the molecular subtypes of gliomas to determine appropriate management strategies for patients. However, genetic testing requires tumor tissue obtained through surgical resection, which imposes a considerable burd...
MammoDenseSegNet: A Context-Aware Deep Learning Model for Dense Tissue Segmentation in Digital Mammograms [0.03%]
一种基于深度学习的数字乳腺X线图像致密乳腺组织分割模型：MammoDenseSegNet
Razieh Ganjee,Andriy Bandos,Md Belayat Hossain et al.
Razieh Ganjee et al.
Breast density is a breast cancer risk factor. The accurate quantification of breast density requires reliable segmentation of dense tissue in mammograms, but it is a challenging task due to large variations in tissue appearance across hosp...
A Novel ConvNeXtV2-MIL Approach for Accurate and Efficient Classification of Microscopic Fungal Morphology [0.03%]
一种新颖的ConvNeXtV2-MIL方法，用于准确高效地分类显微真菌形态
Nuray Ari,Mehmet Erten,Sümeyra Kayali et al.
Nuray Ari et al.
Fungal infections, especially in people with weakened immune systems, are a significant global health burden. Accurate identification of fungal morphology from microscopic images is a critical step in guiding timely antifungal treatment dec...
An Intelligence-Based Hybrid CNN-GAT Framework Optimized by the Whale Optimization Algorithm for Clinical Lung Cancer Classification from Chest CT Images [0.03%]
一种基于鲸鱼优化算法的集成CNN-GAT智能框架，用于胸部CT图像临床肺癌分类
Abbas Mirzaei,Aminreza Mohajerzadeh,Babak Nouri-Moghaddam et al.
Abbas Mirzaei et al.
Early and accurate detection of lung cancer remains a central challenge in intelligence-based medicine, where robust imaging informatics solutions are required to interpret complex chest CT data. This study proposes a novel hybrid convoluti...