Cutting Edge to Cutting Time: Can ChatGPT Improve the Radiologist's Reporting? [0.03%]
从尖端到节省时间：ChatGPT能否改善放射科医生的报告？
Rayan A Ahyad,Yasir Zaylaee,Tasneem Hassan et al.
Rayan A Ahyad et al.
Radiology-structured reports (SR) have many advantages over free text (FT), but the wide implementation of SR is still lagging. A powerful tool such as GPT-4 can address this issue. We aim to employ a web-based reporting tool powered by GPT...
Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI [0.03%]
基于MRI的半月板损伤的完全和弱监督深度学习分类及定位
Kexin Jiang,Yuhan Xie,Xintao Zhang et al.
Kexin Jiang et al.
Meniscal injury is a common cause of knee joint pain and a precursor to knee osteoarthritis (KOA). The purpose of this study is to develop an automatic pipeline for meniscal injury classification and localization using fully and weakly supe...
Balancing Performance and Interpretability in Medical Image Analysis: Case study of Osteopenia [0.03%]
医学影像分析中性能与可解释性的权衡：骨质减少症的案例研究
Mateo Mikulić,Dominik Vičević,Eszter Nagy et al.
Mateo Mikulić et al.
Multiple studies within the medical field have highlighted the remarkable effectiveness of using convolutional neural networks for predicting medical conditions, sometimes even surpassing that of medical professionals. Despite their great p...
US & MR/CT Image Fusion with Markerless Skin Registration: A Proof of Concept [0.03%]
无标志物皮肤配准的US与MR/CT图像融合技术的概念验证研究
Martina Paccini,Giacomo Paschina,Stefano De Beni et al.
Martina Paccini et al.
This paper presents an innovative automatic fusion imaging system that combines 3D CT/MR images with real-time ultrasound acquisition. The system eliminates the need for external physical markers and complex training, making image fusion fe...
Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity [0.03%]
基线水平肺癌患者体内病灶间异质性的影像组学测量及评价方法的建立和评估
Mathilde Lafon,Sophie Cousin,Mélissa Alamé et al.
Mathilde Lafon et al.
Radiomics has traditionally focused on individual tumors, often neglecting the integration of metastatic disease, particularly in patients with non-small cell lung cancer. This study sought to examine intra-patient inter-tumor lesion hetero...
Multi-parameter MRI-Based Machine Learning Model to Evaluate the Efficacy of STA-MCA Bypass Surgery for Moyamoya Disease: A Pilot Study [0.03%]
基于多参数MRI的机器学习模型评估STA-MCA旁路手术治疗 moyamoya 病疗效的价值：一项前瞻性研究
Huaizhen Wang,Jizhen Li,Jinming Chen et al.
Huaizhen Wang et al.
Superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery represents the primary treatment for Moyamoya disease (MMD), with its efficacy contingent upon collateral vessel development. This study aimed to develop and valida...
Diagnostic Accuracy of Ultra-Low Dose CT Compared to Standard Dose CT for Identification of Fresh Rib Fractures by Deep Learning Algorithm [0.03%]
基于深度学习算法的超低剂量CT与常规剂量CT在肋骨骨折诊断中的准确性比较研究
Peikai Huang,Hongyi Li,Fenghuan Lin et al.
Peikai Huang et al.
The present study aimed to evaluate the diagnostic accuracy of ultra-low dose computed tomography (ULD-CT) compared to standard dose computed tomography (SD-CT) in discerning recent rib fractures using a deep learning algorithm detection of...
Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification [0.03%]
美国国家癌症研究所2023年医学图像去识别虚拟研讨会综述 第一部分：MIDI工作组报告- 去识别最佳实践与建议、传统方法工具、国际去识别方法及业界 panel 讨论
David Clunie,Fred Prior,Michael Rutherford et al.
David Clunie et al.
De-identification of medical images intended for research is a core requirement for data-sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and...
CapNet: An Automatic Attention-Based with Mixer Model for Cardiovascular Magnetic Resonance Image Segmentation [0.03%]
基于自动注意力机制的混合模型心血管磁共振图像分割方法研究（CapNet）
Tien Viet Pham,Tu Ngoc Vu,Hoang-Minh-Quang Le et al.
Tien Viet Pham et al.
Deep neural networks have shown excellent performance in medical image segmentation, especially for cardiac images. Transformer-based models, though having advantages over convolutional neural networks due to the ability of long-range depen...
Sunyi Zheng,Xiaonan Cui,Yuxuan Sun et al.
Sunyi Zheng et al.
Accurate image classification and retrieval are of importance for clinical diagnosis and treatment decision-making. The recent contrastive language-image pre-training (CLIP) model has shown remarkable proficiency in understanding natural im...