日期 影响因子
日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:
–

Two-Step Semiautomated Classification of Choroidal Metastases on MRI: Orbit Localization via Bounding Boxes Followed by Binary Classification via Evolutionary Strategies

基于磁共振成像的脉络膜转移瘤两步半自动分类：首先通过边界框进行眼眶定位，然后通过进化策略进行二元分类

期刊:American Journal of Neuroradiology
影响因子:3.7
doi:10.3174/ajnr.A8998
Shi, Jeffrey S; McRae-Posani, Bala; Haque, Sofia; Holodny, Andrei; Shalu, Hrithwik; Stember, Joseph
发表日期：2026
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Automated Midline Shift Detection in Head CT Using Localization and Symmetry Techniques Based on User-Selected Slice

基于用户选择切片的定位和对称技术在头部CT中自动检测中线移位

期刊:American Journal of Neuroradiology
影响因子:3.7
doi:10.3174/ajnr.A8767
Banayan, Nooriel E; Shalu, Hrithwik; Hatzoglou, Vaios; Swinburne, Nathaniel; Holodny, Andrei; Zhang, Zhigang; Stember, Joseph
发表日期：2025
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Evolutionary Strategies Enable Systematic and Reliable Uncertainty Quantification: A Proof-of-Concept Pilot Study on Resting-State Functional MRI Language Lateralization

进化策略实现系统可靠的不确定性量化：静息态功能磁共振成像语言侧化概念验证试点研究

期刊:
影响因子:
doi:10.1007/s10278-024-01188-6
Stember, Joseph N; Dishner, Katharine; Jenabi, Mehrnaz; Pasquini, Luca; K Peck, Kyung; Saha, Atin; Shah, Akash; O'Malley, Bernard; Ilica, Ahmet Turan; Kelly, Lori; Arevalo-Perez, Julio; Hatzoglou, Vaios; Holodny, Andrei; Shalu, Hrithwik
发表日期：2025
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Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection

进化策略人工智能解决深度学习部署中的多项技术挑战：神经母细胞瘤脑转移检测的概念验证演示

期刊:
影响因子:
doi:10.1007/s10278-024-01165-z
Purkayastha, Subhanik; Shalu, Hrithwik; Gutman, David; Holodny, Andrei; Modak, Shakeel; Basu, Ellen; Kushner, Brian; Kramer, Kim; Haque, Sofia; Stember, Joseph N
神经科学
细胞生物学
发表日期：2024
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Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution

利用深度神经进化直接评估脑转移疾病的治疗反应

期刊:Journal of Digital Imaging
影响因子:3.8
doi:10.1007/s10278-022-00725-5
Stember, Joseph N; Young, Robert J; Shalu, Hrithwik
神经科学
发表日期：2023
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Deep Reinforcement Learning with Automated Label Extraction from Clinical Reports Accurately Classifies 3D MRI Brain Volumes

利用深度强化学习和临床报告自动标签提取技术，可准确分类三维磁共振脑部体积

期刊:Journal of Digital Imaging
影响因子:3.8
doi:10.1007/s10278-022-00644-5
Stember, Joseph Nathaniel; Shalu, Hrithwik
神经科学
发表日期：2022
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