Author-Centric AI Pre-Review: Interpreting Science Before It Is Judged [0.03%]
以作者为中心的AI预审：在科学被评判之前进行解读
Bennett A Landman
Bennett A Landman
The editorial explores an author-centric approach to AI in scientific publishing, arguing for the use of AI as a pre-submission self-review tool to help authors anticipate interpretation, clarify arguments, and strengthen rigor, while prese...
Golriz Hosseinimanesh,Farida Cheriet,Ammar Alsheghri et al.
Golriz Hosseinimanesh et al.
Purpose: Deep learning algorithms offer the potential to automate dental crown generation, reducing time-intensive manual design in dental laboratories. However, achieving crowns suitable for direct clinical use requires ...
Head-to-head comparisons of breast density assessment models using deep learning on digital and synthetic mammograms [0.03%]
基于数字和合成乳腺X线影像的深度学习乳腺密度评估模型的对比研究
Krisha Anant,Juanita Hernández López,Junjie Cui et al.
Krisha Anant et al.
Purpose: We aim to evaluate the performance of different deep learning (DL) architectures in breast density classification using digital mammograms (DMs) and synthetic mammograms (SMs) from digital breast tomosynthesis (D...
MILU: a consensus ensemble benchmark for multimodal medical imaging lecture understanding [0.03%]
MILU：多模态医学影像讲座理解的共识集合基准
Md Motaleb Hossen Manik,Md Zabirul Islam,Ge Wang
Md Motaleb Hossen Manik
Purpose: Vision-language models (VLMs) are increasingly used to interpret multimodal educational materials, yet their reliability on diagram-, equation-, and text-dense scientific lecture slides remains poorly understood....
Filter2Noise: a framework for interpretable and zero-shot low-dose CT image denoising [0.03%]
基于解释性和零样本特性的低剂量CT图像降噪框架
Yipeng Sun,Linda-Sophie Schneider,Siyuan Mei et al.
Yipeng Sun et al.
Purpose: Deep learning has achieved remarkable progress in low-dose computed tomography (LDCT) denoising; however, radiologists struggle to trust black-box models they cannot verify or control. Zero-shot methods eliminate...
Parameter-efficient deep-learning-based model for segmentation with radiomic feature extraction [0.03%]
基于参数高效微调的端到端分割与影像组学模型
Daniel Sleiman,Navchetan Awasthi
Daniel Sleiman
Purpose: Magnetic resonance imaging (MRI), particularly dynamic contrast-enhanced MRI (DCE-MRI), plays a vital role in breast cancer assessment by highlighting tumor regions. Accurate 3D segmentation of tumors can signifi...
Charles Guan,Alexander P Rockhill,Masashi Sode et al.
Charles Guan et al.
Purpose: Volumetric ultrafast ultrasound produces massive datasets with high frame rates, dense reconstruction grids, and large channel counts. Beamforming computational demands limit research throughput and prevent real-...
In search of truth: evaluating concordance of AI-based anatomy segmentation models [0.03%]
寻找真相：评估基于人工智能的解剖分割模型的一致性
Lena Giebeler,Deepa Krishnaswamy,David Clunie et al.
Lena Giebeler et al.
Purpose: Artificial intelligence based methods for anatomy segmentation can help automate characterization of large imaging datasets. The growing number of similar functionality models raises the challenge of evaluating t...
Challenging Hounsfield Unit cutoffs: spectral thresholding for synthetic coronary plaque phantoms on photon-counting CT [0.03%]
挑战霍索恩斯单位截止值：光子计数CT上合成冠状动脉斑块模型的谱阈值法
Florian Goldmann,Michael Wels,Thomas Allmendinger et al.
Florian Goldmann et al.
Purpose: Assess whether photon-counting computed tomography (PCCT) improves discrimination of vulnerable coronary soft-plaque components by extending one-dimensional Hounsfield Unit (HU) thresholding to a simple, interpre...
Accuracy and reliability of artery-vein differentiation in small-field macular OCT angiography [0.03%]
小视场黄斑区光学相干断层血管造影的动脉静脉鉴别准确性及可靠性
Haneen Alfauri,Tugce Ilayda Turer,Cyriac Manjaly et al.
Haneen Alfauri et al.
Purpose: Accurate artery-vein (AV) differentiation in small-field macular optical coherence tomography angiography (OCTA) remains challenging due to a lack of standardized guidelines. We propose and validate criteria for ...