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

Pharmacodynamic activity of BMS-986156, a glucocorticoid-induced TNF receptor-related protein agonist, alone or in combination with nivolumab in patients with advanced solid tumors

BMS-986156（一种糖皮质激素诱导的TNF受体相关蛋白激动剂）单独使用或与纳武利尤单抗联合使用治疗晚期实体瘤患者的药效学活性

期刊:ESMO Open
影响因子:8.3
doi:10.1016/j.esmoop.2023.100784
Wang, R; Baxi, V; Li, Z; Locke, D; Hedvat, C; Sun, Y; Walsh, A M; Shao, X; Basavanhally, T; Greenawalt, D M; Patah, P; Novosiadly, R
信号转导
多发性硬化症
免疫/内分泌
发表日期：2023
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Autoimmune gene expression profiling of fingerstick whole blood in Chronic Fatigue Syndrome

慢性疲劳综合征患者指尖全血自身免疫基因表达谱分析

期刊:Journal of Translational Medicine
影响因子:7.5
doi:10.1186/s12967-022-03682-3
Wang, Zheng; Waldman, Michelle F; Basavanhally, Tara J; Jacobs, Aviva R; Lopez, Gonzalo; Perichon, Regis Y; Ma, Johnny J; Mackenzie, Elyse M; Healy, James B; Wang, Yixin; Hersey, Sarah A
免疫/内分泌
发表日期：2022
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New Technologies to Image Tumors

肿瘤成像新技术

期刊:Cancer Treatment and Research
影响因子:4.1
doi:10.1007/978-3-030-38862-1_2
George McNamara, Justin Lucas, John F Beeler, Ajay Basavanhally, George Lee, Cyrus V Hedvat, Vipul A Baxi, Darren Locke, Alexander Borowsky, Richard Levenson
肿瘤
发表日期：2020
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High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection

基于卷积神经网络的高通量自适应采样全切片组织病理图像分析（HASHI）：在浸润性乳腺癌检测中的应用

期刊:PLoS One
影响因子:2.6
doi:10.1371/journal.pone.0196828
Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant; González, Fabio
肿瘤
肿瘤免疫
乳腺癌
发表日期：2018
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Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology

基于稀疏自编码器的染色归一化（StaNoSA）：在数字病理学中的应用

期刊:Computerized Medical Imaging and Graphics
影响因子:4.9
doi:10.1016/j.compmedimag.2016.05.003
Janowczyk, Andrew; Basavanhally, Ajay; Madabhushi, Anant
发表日期：2017
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Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

在全切片图像中准确且可重复地检测浸润性乳腺癌：一种用于量化肿瘤范围的深度学习方法

期刊:Scientific Reports
影响因子:3.9
doi:10.1038/srep46450
Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie N C; Tomaszewski, John; González, Fabio A; Madabhushi, Anant
肿瘤
肿瘤免疫
乳腺癌
发表日期：2017
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Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer

利用有限的训练数据预测分类器性能：在乳腺癌和前列腺癌计算机辅助诊断中的应用

期刊:PLoS One
影响因子:2.6
doi:10.1371/journal.pone.0117900
Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant
肿瘤
前列腺癌
肿瘤免疫
乳腺癌
发表日期：2015
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Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides

用于从整个组织病理切片中区分ER+乳腺癌肿瘤分级的多视野框架

期刊:IEEE Transactions on Biomedical Engineering
影响因子:4.5
doi:10.1109/TBME.2013.2245129
Basavanhally, Ajay; Ganesan, Shridar; Feldman, Michael; Shih, Natalie; Mies, Carolyn; Tomaszewski, John; Madabhushi, Anant
肿瘤
肿瘤免疫
乳腺癌
发表日期：2013
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Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX

基于图像的多参数雌激素受体阳性乳腺癌组织病理学预后预测的多视野策略：与Oncotype DX的比较

期刊:Journal of Pathology Informatics
影响因子:
doi:10.4103/2153-3539.92027
Basavanhally, Ajay; Feldman, Michael; Shih, Natalie; Mies, Carolyn; Tomaszewski, John; Ganesan, Shridar; Madabhushi, Anant
肿瘤
肿瘤免疫
信号转导
乳腺癌
免疫/内分泌
发表日期：2011
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