SparseMed3D: Foundation Models for Sparse Instance Medical Segmentation [0.03%]
SparseMed3D：稀疏实例医学分割基础模型
Erfan Darzidehkalani,Cheng-Huang Hsiao,Rina Bao et al.
Erfan Darzidehkalani et al.
Vision foundation models excel at general segmentation but underperform sharply when positive instances occupy a vanishing fraction of the input-a regime that arises across small-lesion and rare-event medical segmentation, with neonatal hyp...
Domain-Specific Transfer Learning for Gastric Cancer Tissue Classification [0.03%]
基于胃癌组织分类的领域适应迁移学习方法研究
Venkata Ramana Kaneti,Santosh Reddy P,Alpha Vijayan et al.
Venkata Ramana Kaneti et al.
Gastric cancer is the fifth most diagnosed cancer worldwide and is the fifth most deadly. An essential part of the diagnosis and prognosis of cancer is the histopathological tissue classification. Pathologists classify tissue samples, but t...
Deep Learning-Based Automated Reports for Breast Density Assessment in Mammography Images [0.03%]
基于深度学习的乳腺密度评估的 mammography 图像自动报告系统
Juliana H do Prado,Jan Hurtado,Luiz F T Santos et al.
Juliana H do Prado et al.
Breast density is a key factor in mammographic screening, as high-density tissue increases cancer risk and can obscure lesions, reducing diagnostic sensitivity. This work presents a deep learning-based framework for automated breast density...
Multiparametric Characterisation of a Recursive Filtration Algorithm on a Commercial Angiographic Equipment [0.03%]
一种递归滤波算法的多参数表征及其在商用血管造影设备上的实现
Raffaele Villa,Nicoletta Paruccini,Elena De Ponti
Raffaele Villa
Digital fluoroscopy offers high temporal resolution but is limited by substantial noise. Recursive filtration improves the signal-to-noise ratio (SNR) by combining information across frames, though potentially at the cost of motion blur and...
Energy-Efficient Usage of CT Scanners Through Mathematically Optimized Examination Scheduling [0.03%]
通过数学优化的检查预约提高CT扫描仪的能效
Martin Segeroth,Armin Nurkanović,Ashraya Indrakanti et al.
Martin Segeroth et al.
Medical imaging devices are large consumers of electricity. This study aims to estimate potential energy savings through optimized CT examination scheduling using an ideal (hypothetical) lower-bound and a clinically realistic model. Examina...
Domain Generalization Mitigates Scanner-Induced Domain Shift in Medical Imaging [0.03%]
领域泛化缓解医学影像中的扫描仪引起的领域偏移
Dagoberto Pulido-Arias,Mason C Cleveland,Jay Patel et al.
Dagoberto Pulido-Arias et al.
Deep learning models for medical image analysis often fail in clinical deployment due to domain shift from varied acquisition hardware and protocols. We present a comprehensive evaluation of various domain generalization (DG) techniques to ...
A Systematic Review and Meta-analysis of Two-Dimensional Breast Ultrasound Radiomics: Implications for Lesion Characterization and Diagnostic Accuracy [0.03%]
二维乳腺超声影像组学系统综述和元分析：对病变特征描述及诊断准确性的影响
Omar Abd Al Mjed Allasasmeh,Areej H Al-Sarairah,Muath A Odeh Hurani et al.
Omar Abd Al Mjed Allasasmeh et al.
Radiomics applied to two-dimensional breast ultrasound has emerged as a potential noninvasive approach for differentiating benign from malignant breast lesions; however, its diagnostic accuracy and methodological reliability remain uncertai...
Kolmogorov-Arnold Guided Local-Global Attention for Medical Image Classification [0.03%]
基于柯尔莫哥洛夫-阿诺德的局部-全局注意力的医学图像分类方法
Weichao Pan,Xu Wang,Chengze Lv et al.
Weichao Pan et al.
Medical image classification relies on both fine lesion details and broader anatomical context to support reliable clinical decisions. Existing attention mechanisms often capture only one of these aspects, resulting in unstable and incomple...
Artificial Intelligence-Assisted Inner Ear Computed Tomography Analysis: Radiomics-Based Comparison of Affected and Unaffected Ears in Idiopathic Sudden Sensorineural Hearing Loss [0.03%]
基于放射组学的特发性突发性感音神经性聋患耳和非患耳内耳CT影像人工智能辅助分析
Joochan Choi,Woogsang Sunwoo,Kwanggi Kim
Joochan Choi
We aimed to determine whether computed tomography (CT)-based radiomic features of the inner ear can distinguish the affected side from the contralateral normal-hearing side in patients with idiopathic sudden sensorineural hearing loss (ISSN...
High Adoption, Higher Expectations: A Cross-Sectional Survey of Radiologist Engagement with Artificial Intelligence in the United Arab Emirates [0.03%]
高采纳，高期望：阿拉伯联合酋长国放射学家参与人工智能的横断面调查
Ayham Khan Ansari,Abdulrahman Hamshari,Muhammad Kamil Shahbaz et al.
Ayham Khan Ansari et al.
Artificial intelligence (AI) is poised to transform diagnostic radiology, yet data on its adoption and the perspectives of radiologists in the Middle East remain scarce. This study provides the first comprehensive analysis of AI engagement ...