Hannah Gilmore

Corredor-Prada, G., Toro, P., Bera, K., Rasmussen, D., Sankar Viswanathan, V., Buzzy, C., Fu, P., Barton, L., Stroberg, E., Duval, E., Gilmore, H., Mukhopadyay, S., & Madabhushi, A. (2021). Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings. Journal of Medical Imaging, 8 (Suppl 1), 017501.

Qiao, P., Ayat, N., Vaidya, A., Gao, S., Sun, W., Chou, S., Han, Z., Gilmore, H., Winter, J., & Lu, Z. (2020). Magnetic Resonance Molecular Imaging of Extradomain B Fibronectin Improves Imaging of Pancreatic Cancer Tumor Xenografts. Frontiers in Oncology, 10

Vaidya, A., Wang, H., Qian, V., Gilmore, H., & Lu, Z. (2020). Overexpression of Extradomain-B Fibronectin is Associated with Invasion of Breast Cancer Cells. Cells, 9 (8).

Vaidya, A., Ayat, N., Buford, M., Wang, H., Shankardass, A., Zhao, Y., Gilmore, H., Wang, Z., & Lu, Z. (2020). Noninvasive assessment and therapeutic monitoring of drug-resistant colorectal cancer by MR molecular imaging of extradomain-B fibronectin. Theranostics, 10 (24), 11127-11143.

Li, H., Whitney, J., Bera, K., Gilmore, H., Thorat, M., Badve, S., & Madabhushi, A. (2019). Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings. Breast Cancer Research, 21 (1), 114.

Braman, N., Prasanna, P., Whitney, J., Singh, S., Beig, N., Etesami, M., Bates, D., Gallagher, K., Bloch, B., Vulchi, M., Turk, P., Bera, K., Abraham, J., Sikov, W., Somlo, G., Harris, L., Gilmore, H., Plecha, D., Varadan, V., & Madabhushi, A. (2019). Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2) �Positive Breast Cancer. JAMA Network Open, 2 (4), e192561.

Janowczyk, A., Zuo, R., Gilmore, H., Feldman, M., & Madabhushi, A. (2019). HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides. JCO Clinical Cancer Informatics.

Vaidya, A., Sun, Z., Ayat, N., Schilb, A., Liu, X., Jiang, H., Sun, D., Scheidt, J., Qian, V., He, S., Gilmore, H., Schiemann, W., & Lu, Z. (2019). Systemic Delivery of Tumor-Targeting siRNA Nanoparticles against an Oncogenic LncRNA Facilitates Effective Triple-Negative Breast Cancer Therapy. Bioconjugate Chemistry, 30 (3), 907-919.

Xue, Z., Gong, L., Wang, G., Lu, C., Gilmore, H., Zhang, S., & Madabhushi, A. (2019). Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images. Journal of Medical Imaging, 6 (01).

Janowczyk, A., Zuo, R., Gilmore, H., Feldman, M., & Madabhushi, A. (2019). HistoQC: An open-source quality control tool for digital pathology slides. JCO Clinical Cancer Informatics, 3

Whitney, J., Corredor-Prada, G., Janowczyk, A., Ganesan, S., Doyle, S., Tomaszewski, J., Feldman, M., Gilmore, H., & Madabhushi, A. (2018). Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer. BMC Cancer, 18 (1).

Lu, C., Romo-Bucheli, D., WAng, X., Janowczyk, A., Ganesan, S., Gilmore, H., Rimm, D., & Madabhushi, A. (2018). Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers. Laboratory Investigation, 98 (11), 1438-1448.

Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., Madabhushi, A., & Gonz�lez, F. (2018). High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection. PLoS ONE, 13 (5).

Janowczyk, A., Doyle, S., Gilmore, H., & Madabhushi, A. (2018). A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 6 (3), 270-276.

Romo-Bucheli, D., Janowczyk, A., Gilmore, H., Romero, E., & Madabhushi, A. (2017). A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers. Cytometry Part A, 91 (6), 566-573.

Romo-Bucheli, D., Janowczyk, A., Gilmore, H., Romero, E., & Madabhushi, A. (2016). Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images. Scientific Reports, 6

Wan, T., Bloch, B., Plecha, D., Thompson, C., Gilmore, H., Jaffe, C., Harris, L., & Madabhushi, A. (2016). A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores. Scientific Reports [20452322], 6

Lu, C., Xu, H., Xue, Z., Gilmore, H., Mandal, M., & Madabhushi, A. (2016). Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images. Scientific Reports [20452322], 6

Janowczyk, A., Doyle, S., Gilmore, H., & Madabhushi, A. (2016). A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization [21681163].

Xue, Z., Luo, X., Wang, G., Gilmore, H., & Madabhushi, A. (2016). A Deep Convolutional Neural Network for Segmenting and Classifying Epithelial and Stromal Regions in Histopathological Images. Neurocomputing [09252312], 191 , 214-223.

Publications