Bennett A Landman,Ivana Išgum
Bennett A Landman
The editorial announces two pilot initiatives linking SPIE Medical Imaging and the Journal of Medical Imaging: a journal-first conference presentation pathway for recently published JMI papers and a streamlined route for selected conference...
Mask R-CNN-based carotid plaque localization in ultrasound images [0.03%]
基于Mask R-CNN的超声图像颈动脉斑块定位方法
Maxwell J Kiernan,Rashid Al Mukaddim,Carol C Mitchell et al.
Maxwell J Kiernan et al.
Purpose: In this study, we present the refinement of a Mask R-CNN model initially designed for carotid lumen detection to automatically generate bounding boxes (BB) enclosing atherosclerotic plaque. Although the model als...
Charge-cloud-based micrometer resolution in deep-silicon photon-counting CT [0.03%]
基于电荷云的微米分辨率深硅光电计数CT
Rickard Brunskog,Mats Persson,Moa Yveborg Tamm et al.
Rickard Brunskog et al.
Purpose: We are developing a monolithic deep-silicon photon-counting sensor targeting spatial resolution on the order of 1 μ m . This work investigates how pixel pitch, noise level, threshold number, and threshold...
Measured and synthetic rigid head motion datasets via generative model for motion simulation and compensation in medical imaging [0.03%]
基于生成模型的用于医学影像中运动模拟和补偿的刚性头部运动测量及合成数据集
Manuela Goldmann,Felix Damm,Florian Goldmann et al.
Manuela Goldmann et al.
Purpose: Rigid head motion during interventional C-arm cone-beam CT (CBCT) is a major source of image degradation. Learning-based motion estimation requires realistic training data, but ground-truth motion is scarce, limi...
Radiomics-based machine learning to evaluate immunotherapy efficacy in non-small cell lung cancer patients with bone metastases [0.03%]
基于影像组学的机器学习评估非小细胞肺癌骨转移患者免疫治疗效果的研究
Reza Kakavand,Nils D Forkert,Annalise Abbott et al.
Reza Kakavand et al.
Purpose: Assessing treatment response in bone metastases from non-small cell lung cancer (NSCLC) remains a major clinical challenge, particularly for patients receiving immune checkpoint inhibitors (ICIs). The existing re...
OCR-mediated modality dominance in vision-language models: implications for radiology AI trustworthiness [0.03%]
基于 OCR 的模态主导性在视觉语言模型中的作用及对放射科人工智能可信度的启示
Izzet Turkalp Akbasli,Baris Ozturk,Oguzhan Serin et al.
Izzet Turkalp Akbasli et al.
Purpose: Vision-language models (VLMs) are increasingly proposed for radiologic decision support, yet the security implications of deploying optical character recognition (OCR)-capable models in diagnostic workflows remai...
Fairness-aware deep learning for predicting visual field loss from optical coherence tomography [0.03%]
公平感知的深度学习在从光学相干断层扫描预测视野缺损中的应用
Shreeya Pandey,Leila Gheisi,Yu Huang et al.
Shreeya Pandey et al.
Purpose: Standard automated perimetry is the clinical standard for measuring visual field (VF) loss in glaucoma, but it is subjective, time-consuming, and variable across patient visits. In contrast, optical coherence tom...
Image class translation: visual inspection of class-specific hypotheticals and classification based on translation distance [0.03%]
基于翻译距离的类特定假设的视觉检查及分类
Mikyla K Bowen,Jesse W Wilson
Mikyla K Bowen
Purpose: A major barrier to the implementation of artificial intelligence for medical applications is automated CNNs' lack of explainability and high confidence for incorrect decisions, specifically with out-of-domain sam...
Comparison of chest X-ray radiography AI model to comorbidities for predicting intensive care unit admission for COVID-19 [0.03%]
胸部X光影像AI模型与合并症预测COVID-19患者进入重症监护病房的比较研究
Heather M Whitney,Hui Li,Karen Drukker et al.
Heather M Whitney et al.
Purpose: There are practical limitations to using comorbidities alone in predicting the need for admission to the intensive care unit (ICU). We compared the classification performance of a deep learning/artificial intelli...
Sam Bogdanov,Andre Teixeira da Silva Hucke,Bennett A Landman
Sam Bogdanov
This editorial revisits the enduring role of literature reviews in scientific training and communication, emphasizing that their value lies in expert synthesis rather than simple aggregation of sources. In an era of AI-assisted discovery an...