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

Personalizing Seizure Detection for Individual Patients by Optimal Selection of EEG Signals

通过优化选择脑电信号,实现个体化癫痫发作检测

Ferrara, Rosanna; Giaquinto, Martino; Percannella, Gennaro; Rundo, Leonardo; Saggese, Alessia

Imaging-based assessment of response to olaparib in platinum-sensitive relapsed ovarian cancer patients

基于影像学的奥拉帕尼疗效评估在铂敏感复发性卵巢癌患者中的应用

Delgado-Ortet, Maria; Bura, Vlad; Funingana, Ionut-Gabriel; Hulse, David; Rundo, Leonardo; Brenton, James D; Sala, Evis; Escudero Sanchez, Lorena

Assessment of early response to neoadjuvant chemotherapy in multi-site high-grade serous ovarian cancer using hyperpolarized-(13)C MRI

利用超极化-(13)C MRI评估多部位高级别浆液性卵巢癌新辅助化疗的早期疗效

Beer, Lucian; Bura, Vlad; Ursprung, Stephan; Woitek, Ramona; McLean, Mary A; Ang, Joo Ern; Jimenez-Linan, Mercedes; Gill, Andrew B; Kaggie, Joshua; Locke, Matthew; Frary, Amy; Field-Rayner, Johanna; Patterson, Ilse; Reinius, Marika; Graves, Martin J; Deen, Surrin; Funingana, Gabriel; Rundo, Leonardo; Priest, Andrew; Aloj, Luigi; Manavaki, Roido; Mendichovszky, Iosif A; Robb, Fraser; Schulte, Rolf F; Couturier, Dominique-Laurent; D'Santos, Clive S; Franklin, Valar; Kishore, Kamal; Allajbeu, Iris; Sauer, Carolin; Gallagher, Ferdia A; Brindle, Kevin M; Brenton, James D; Sala, Evis

Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study

基于人工智能的半自动分割方法用于提取乳腺癌超声放射组学特征:一项前瞻性多中心研究

Bartolotta, Tommaso Vincenzo; Militello, Carmelo; Prinzi, Francesco; Ferraro, Fabiola; Rundo, Leonardo; Zarcaro, Calogero; Dimarco, Mariangela; Orlando, Alessia Angela Maria; Matranga, Domenica; Vitabile, Salvatore

Reply to a Letter to the Editor on Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review

回复编辑来信,主题为“全自动和半自动人工智能方法在MRI上检测临床显著性前列腺癌的性能比较:一项系统性综述”。

Sushentsev, Nikita; Barrett, Tristan; Rundo, Leonardo

Image biomarkers and explainable AI: handcrafted features versus deep learned features

图像生物标志物和可解释人工智能:手工特征与深度学习特征的比较

Rundo, Leonardo; Militello, Carmelo

Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability

MRI放射组学特征在识别颈动脉斑块易损性方面的稳健性和分类能力

Meddings, Zakaria; Rundo, Leonardo; Sadat, Umar; Zhao, Xihai; Teng, Zhongzhao; Graves, Martin J

IMPA-Net: Interpretable Multi-Part Attention Network for Trustworthy Brain Tumor Classification from MRI

IMPA-Net:基于MRI的可解释多部分注意力网络,用于可信的脑肿瘤分类

Xie, Yuting; Zaccagna, Fulvio; Rundo, Leonardo; Testa, Claudia; Zhu, Ruifeng; Tonon, Caterina; Lodi, Raffaele; Manners, David Neil

Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach

利用机器学习从CT血管造影预测颈动脉症状:一种放射组学和深度学习方法

Le, Elizabeth P V; Wong, Mark Y Z; Rundo, Leonardo; Tarkin, Jason M; Evans, Nicholas R; Weir-McCall, Jonathan R; Chowdhury, Mohammed M; Coughlin, Patrick A; Pavey, Holly; Zaccagna, Fulvio; Wall, Chris; Sriranjan, Rouchelle; Corovic, Andrej; Huang, Yuan; Warburton, Elizabeth A; Sala, Evis; Roberts, Michael; Schönlieb, Carola-Bibiane; Rudd, James H F

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

整合放射基因组学模型可预测高级别浆液性卵巢癌对新辅助化疗的反应

Crispin-Ortuzar, Mireia; Woitek, Ramona; Reinius, Marika A V; Moore, Elizabeth; Beer, Lucian; Bura, Vlad; Rundo, Leonardo; McCague, Cathal; Ursprung, Stephan; Escudero Sanchez, Lorena; Martin-Gonzalez, Paula; Mouliere, Florent; Chandrananda, Dineika; Morris, James; Goranova, Teodora; Piskorz, Anna M; Singh, Naveena; Sahdev, Anju; Pintican, Roxana; Zerunian, Marta; Rosenfeld, Nitzan; Addley, Helen; Jimenez-Linan, Mercedes; Markowetz, Florian; Sala, Evis; Brenton, James D