Sureness of classification of breast cancers as pure ductal carcinoma in situ or with invasive components on dynamic contrast-enhanced magnetic resonance imaging: application of likelihood assurance metrics for computer-aided diagnosis [0.03%]
动态对比增强磁共振成像上导管原位癌分类的确证性研究：计算辅助诊断中应用可能性指标法
Heather M Whitney,Karen Drukker,Alexandra Edwards et al.
Heather M Whitney et al.
Purpose: Breast cancer may persist within milk ducts (ductal carcinoma in situ, DCIS) or advance into surrounding breast tissue (invasive ductal carcinoma, IDC). Occasionally, invasiveness in cancer may be underestimated ...
Naeeme Modir,Maysam Shahedi,James Dormer et al.
Naeeme Modir et al.
Purpose: This study demonstrates the feasibility of using an LED array for hyperspectral imaging (HSI). The prototype validates the concept and provides insights into the design of future HSI applications. Our goal is to ...
Image database with slides prepared by the Ziehl-Neelsen method for training automated detection and counting systems for tuberculosis bacilli [0.03%]
一套用于训练结核分枝杆菌自动化检测和计数系统的扎伊尔-尼elson法幻灯片图像数据库
João Victor Boechat Gomide,Thales Francisco Mota Carvalho,Élida Aparecida Leal et al.
João Victor Boechat Gomide et al.
Purpose: We aim to provide a robust dataset for training automated systems to detect tuberculosis bacilli using Ziehl-Neelsen stained slides. By making this dataset available, a critical gap in the availability of public ...
Comparing percent breast density assessments of an AI-based method with expert reader estimates: inter-observer variability [0.03%]
基于人工智能的方法与专家阅片人乳腺密度评估的对比：组间变异系数分析
Stepan Romanov,Sacha Howell,Elaine Harkness et al.
Stepan Romanov et al.
Purpose: Breast density estimation is an important part of breast cancer risk assessment, as mammographic density is associated with risk. However, density assessed by multiple experts can be subject to high inter-observe...
Asymmetric scatter kernel estimation neural network for digital breast tomosynthesis [0.03%]
用于数字乳腺体层摄影的非对称散射核估计神经网络
Subong Hyun,Seoyoung Lee,Ilwong Choi et al.
Subong Hyun et al.
Purpose: Various deep learning (DL) approaches have been developed for estimating scatter radiation in digital breast tomosynthesis (DBT). Existing DL methods generally employ an end-to-end training approach, overlooking ...
Self-supervision enhances instance-based multiple instance learning methods in digital pathology: a benchmark study [0.03%]
自监督增强实例基于多示例学习方法在数字病理中的基准研究
Ali Mammadov,Loïc Le Folgoc,Julien Adam et al.
Ali Mammadov et al.
Purpose: Multiple instance learning (MIL) has emerged as the best solution for whole slide image (WSI) classification. It consists of dividing each slide into patches, which are treated as a bag of instances labeled with ...
Deep learning-based temporal MR image reconstruction for accelerated interventional imaging during in-bore biopsies [0.03%]
基于深度学习的加速介入成像期间体内活检的时间MR图像重建
Constant R Noordman,Steffan J W Borgers,Martijn F Boomsma et al.
Constant R Noordman et al.
Purpose: Interventional MR imaging struggles with speed and efficiency. We aim to accelerate transrectal in-bore MR-guided biopsies for prostate cancer through undersampled image reconstruction and instrument localization...
Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification [0.03%]
高效同态加密的基于联邦学习的糖尿病视网膜病变分类方法
Christopher Nielsen,Matthias Wilms,Nils D Forkert
Christopher Nielsen
Purpose: Diabetic retinopathy (DR) is the leading cause of blindness among working-age adults globally. Although machine learning (ML) has shown promise for DR diagnosis, ensuring model generalizability requires training ...
Introduction to the Special Issue: Celebrating Digital Tomosynthesis-Past, Present, and Future [0.03%]
专刊介绍: 庆祝数字断层合成技术——过去、现在和未来
Stephen J Glick,Ingrid S Reiser,Mitchell M Goodsitt et al.
Stephen J Glick et al.
JMI guest editors present a diverse collection of research and perspectives in a special issue celebrating the past, present, and future of digital tomosynthesisan imaging modality that continues to grow in both clinical relevance and techn...
Robust evaluation of tissue-specific radiomic features for classifying breast tissue density grades [0.03%]
鲁棒性评估组织特异性放射组学特征以分类乳腺组织密度等级
Vincent Dong,Walter Mankowski,Telmo M Silva Filho et al.
Vincent Dong et al.
Purpose: Breast cancer risk depends on an accurate assessment of breast density due to lesion masking. Although governed by standardized guidelines, radiologist assessment of breast density is still highly variable. Autom...