Self-supervised learning for chest computed tomography: training strategies and effect on downstream applications [0.03%]
胸部CT的自监督学习：训练策略及其对下游任务的影响
Amara Tariq,Gokul Ramasamy,Bhavik Patel et al.
Amara Tariq et al.
Purpose: Self-supervised pre-training can reduce the amount of labeled training data needed by pre-learning fundamental visual characteristics of the medical imaging data. We investigate several self-supervised training s...
Centerline-guided reinforcement learning model for pancreatic duct identifications [0.03%]
基于中心线的胰管识别增强学习模型
Sepideh Amiri,Reza Karimzadeh,Tomaž Vrtovec et al.
Sepideh Amiri et al.
Purpose: Pancreatic ductal adenocarcinoma is forecast to become the second most significant cause of cancer mortality as the number of patients with cancer in the main duct of the pancreas grows, and measurement of the pa...
Yihao Liu,Junyu Chen,Lianrui Zuo et al.
Yihao Liu et al.
Purpose: Deformable image registration establishes non-linear spatial correspondences between fixed and moving images. Deep learning-based deformable registration methods have been widely studied in recent years due to th...
Data-driven nucleus subclassification on colon hematoxylin and eosin using style-transferred digital pathology [0.03%]
基于风格迁移的数字病理学下的结肠HE染色核分类子分级方法研究
Lucas W Remedios,Shunxing Bao,Samuel W Remedios et al.
Lucas W Remedios et al.
Purpose: Cells are building blocks for human physiology; consequently, understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions in both hea...
Demystifying the effect of receptive field size in U-Net models for medical image segmentation [0.03%]
揭秘U形网络模型中感受野大小在医学图像分割中的作用
Vincent Loos,Rohit Pardasani,Navchetan Awasthi
Vincent Loos
Purpose: Medical image segmentation is a critical task in healthcare applications, and U-Nets have demonstrated promising results in this domain. We delve into the understudied aspect of receptive field (RF) size and its ...
ChatGP-Me? [0.03%]
聊天GP-我？
Elias Levy,Bennett Landman
Elias Levy
The editorial evaluates how the GenAI technologies available in 2024 (without specific coding) could impact scientific processes, exploring two AI tools with the aim of demonstrating what happens when using custom LLMs in five research lab ...
Micael Oliveira Diniz,Mohammad Khalil,Erika Fagman et al.
Micael Oliveira Diniz et al.
Purpose: We aim to investigate the localization, visibility, and measurement of lung nodules in digital chest tomosynthesis (DTS). Approach: ...
Our journey toward implementation of digital breast tomosynthesis in breast cancer screening: the Malmö Breast Tomosynthesis Screening Project [0.03%]
我们进行乳腺癌筛查的数字乳腺体层摄影检测的过程——马尔默乳腺体层摄影筛查项目
Anders Tingberg,Victor Dahlblom,Magnus Dustler et al.
Anders Tingberg et al.
Purpose: The purpose is to describe the Malmö Breast Tomosynthesis Screening Project from the beginning to where we are now, and thoughts for the future. ...
Expanding generalized contrast-to-noise ratio into a clinically relevant measure of lesion detectability by considering size and spatial resolution [0.03%]
通过考虑大小和空间分辨率将广义对比噪声比扩展为临床相关的病变检测度量标准
Siegfried Schlunk,Brett Byram
Siegfried Schlunk
Purpose: Early image quality metrics were often designed with clinicians in mind, and ideal metrics would correlate with the subjective opinion of practitioners. Over time, adaptive beamformers and other post-processing m...
Photon-counting computed tomography versus energy-integrating computed tomography for detection of small liver lesions: comparison using a virtual framework imaging [0.03%]
基于虚拟框架成像的光子计数CT与能量积分CT检测小肝病灶的比较研究
Nicholas Felice,Benjamin Wildman-Tobriner,William Paul Segars et al.
Nicholas Felice et al.
Purpose: Photon-counting computed tomography (PCCT) has the potential to provide superior image quality to energy-integrating CT (EICT). We objectively compare PCCT to EICT for liver lesion detection. ...