Illustration of transfer learning from breast cancer detection to risk prediction: adaptation to local data and local objectives [0.03%]
乳腺癌检测向风险预测的迁移学习示例：适应本地数据和本地目标
Tobias Wagner,Zan Klanecek,Yao-Kuan Wang et al.
Tobias Wagner et al.
Purpose: We aim to investigate whether a breast cancer risk model can be trained with transfer learning from a breast cancer detection model. Approach: ...
RadGazeGen: radiomics and gaze-guided chest X-ray generation using diffusion models [0.03%]
基于扩散模型的放射组学和注视引导的胸部X光图像生成（RadGazeGen）
Moinak Bhattacharya,Gagandeep Singh,Shubham Jain et al.
Moinak Bhattacharya et al.
Purpose: We present RadGazeGen, a framework for integrating experts' eye gaze patterns and radiomic feature maps as controls within text-to-image diffusion models to enable high-fidelity medical image generation. Although...
DDARes-U2Net: a dual-decoder adversarial residual U2Net algorithm for segmentation of COVID-19 pneumonia lesions [0.03%]
DDARes-U2Net算法：一种用于COVID-19肺炎病灶分割的双解码器对抗残差U2Net算法
Xiao Li,Fujiao Ju,Yifei Xu et al.
Xiao Li et al.
Purpose: We aim to overcome the remaining bottlenecks in COVID-19 lesion segmentation from chest CT-namely, blurred lesion boundaries, false-positive responses from vessels or trachea, and the extreme variability of lesio...
High-speed optical tracking and augmented reality platform for image-guided interventions [0.03%]
用于图像引导干预的高速光学跟踪与增强现实平台
Nati Nawawithan,James Yu,Kelden Pruitt et al.
Nati Nawawithan et al.
Purpose: During interventional procedures, clinicians need to mentally register anatomical information from preoperative cross-sectional images onto the patient's body to envision the location of subsurface targets and cr...
Transplant-ready? Evaluating AI lung segmentation models in candidates with severe lung disease [0.03%]
准备移植？评估AI肺分割模型在严重肺疾病患者中的应用
Jisoo Lee,Michael R Harowicz,Yuwen Chen et al.
Jisoo Lee et al.
Purpose: This study evaluates publicly available deep-learning-based lung segmentation models in transplant-eligible patients to determine their performance across disease severity levels, pathology categories, and lung s...
Methodological considerations for evaluating deep learning segmentation models in digital pathology whole-slide images [0.03%]
数字病理学全片图像中评估深度学习分割模型的方法论考虑因素
Arian Arab,Victor Garcia,Seyed Kahaki et al.
Arian Arab et al.
Purpose: Automated whole-slide image (WSI) analysis, specifically applications of deep learning (DL)-based algorithms, has been enabling automated detection, classification, segmentation, and prognosis for various disease...
Application of learned ideal observers for estimating task-based performance bounds for computed imaging systems [0.03%]
基于学习的理想观察者在计算型成像系统任务性能上限估计中的应用研究
Kaiyan Li,Umberto Villa,Hua Li et al.
Kaiyan Li et al.
Purpose: The performance of the ideal observer (IO) acting on imaging measurements has long been advocated as a figure-of-merit (FOM) to guide the optimization of imaging systems. For computed imaging systems, the perform...
Generalizations of the Jaccard index and Sørensen index for assessing agreement across multiple readers in object detection and instance segmentation in biomedical imaging [0.03%]
用于生物医学影像中多阅片人检测和实例分割的一致性评估的Jaccard指数与Sørensen指数的扩展形式
Madeleine S Durkee,Kyle Lleras,Karen Drukker et al.
Madeleine S Durkee et al.
Significance: Manual annotations are necessary for training supervised learning algorithms for object detection and instance segmentation. These manual annotations are difficult to acquire, noisy, and inconsistent across ...
Deep-learning-based multiple organ segmentation for CT scout images: applications to automatic CT planning [0.03%]
基于深度学习的CT定位像多器官分割：应用于自动CT计划
Kaylee W Fang,Sen Wang,Maria Jose Medrano et al.
Kaylee W Fang et al.
Purpose: Computed tomography (CT) scout images are used in CT planning to set the anatomic scan range and optimize the radiation dose. Manual exam planning is highly variable and contributes to excess radiation dose deliv...
Avanith Kanamarlapudi,Ryan Zurrin,Edward Gaibor et al.
Avanith Kanamarlapudi et al.
Purpose: Public datasets for training artificial intelligence (AI) models in breast cancer screening are limited in size and quality, making it difficult to develop reliable systems. We introduce OMAMA-DB, an extensive pu...