Yang, S., Tao, G., Zhang, Y., & Lin, W.(2018).Adaptive Regulation of Hypersonic Vehicle Systems with Partial Nonlinear Parametrization.Proc. of American Control Conference (2018).
Ji, J., Luo, C., Chen, X., Yu, L., & Li, P.(2018).Cross-Domain Sentiment Classification via A Bifurcation-LSTM.Proceeding of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'18).
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).
Bera, K., Velcheti, V., & Madabhushi, A.(2018).Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications.American Society of Clinical Oncology Educational Book.
WAng, X., Barrera, C., Velu, P., Bera, K., Prasanna, P., Khunger, M., Khunger, A., Velcheti, V., & Madabhushi, A.(2018).Computer extracted features of cancer nuclei from H&E stained tissues of tumor predicts response to nivolumab in non-small cell lung cancer..Journal of Clinical Oncology,36(15_suppl),12061-12061.
Patil, P., Bera, K., Vaidya, P., Prasanna, P., Khunger, M., Khunger, A., Velcheti, V., & Madabhushi, A.(2018).Correlation of radiomic features with PD-L1 expression in early stage non-small cell lung cancer (ES-NSCLC) to predict recurrence and overall survival (OS)..Journal of Clinical Oncology,36(15_suppl),e24247-e24247.
Barrera, C., Velu, P., Bera, K., WAng, X., Prasanna, P., Khunger, M., Khunger, A., Velcheti, V., Romero, E., & Madabhushi, A.(2018).Computer-extracted features relating to spatial arrangement of tumor infiltrating lymphocytes to predict response to nivolumab in non-small cell lung cancer (NSCLC)..Journal of Clinical Oncology,36(15_suppl),12115-12115.
Verma, N., Harding, D., Mohammadi, A., Goldstein, L., Gilmore, H., Feldman, M., Tomaszewski, J., Basavanhally, A., Lloyd, M., Fu, P., Ganesan, S., Davidson, N., Madabhushi, A., & Monaco, J.(2018).Image-based risk score to predict recurrence of ER+ breast cancer in ECOG-ACRIN Cancer Research Group E2197..Journal of Clinical Oncology,36(15_suppl),540-540.
Khorrami, M., Jain, P., Khunger, M., Ahmad, U., Stephans, K., Murthy, S., Velcheti, V., & Madabhushi, A.(2018).Combination of CT derived radiomic features and lymphovascular invasion status to predict disease recurrence following trimodality therapy in non-small cell lung cancer..Journal of Clinical Oncology,36(15_suppl),e24314-e24314.
Bhargava, H., Leo, P., Elliott, R., Janowczyk, A., Whitney, J., Gupta, S., Yamoah, K., Rebbeck, T., Feldman, M., Lal, P., & Madabhushi, A.(2018).Computer-extracted stromal features of African-Americans versus Caucasians from H&E slides and impact on prognosis of biochemical recurrence..Journal of Clinical Oncology,36(15_suppl),12075-12075.
Braman, N., Ravichandran, K., Janowczyk, A., Abraham, J., & Madabhushi, A.(2018).Predicting neo-adjuvant chemotherapy response from pre-treatment breast MRI using machine learning and HER2 status..Journal of Clinical Oncology,36(15_suppl),582-582.
Shiradkar, R., Ghose, S., Jambor, I., Taimen, P., Ettala, O., Purysko, A., & Madabhushi, A.(2018).Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings: Prostate Cancer Recurrence Prediction.Journal of Magnetic Resonance Imaging.
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.
Akram, N., Madanayake, A., Handagala, S., Mandal, S., & Belostotski, L.(2018).Multiport ADCs for Microwave Focal Plane Array Dish Receivers.IEEE International Symposium on Circuits and Systems (ISCAS).
Mohseni, P.(2018).Modeling and characterization of capacitive elements with tissue as dielectric material for wireless powering of neural implants.IEEE Trans. Neural Syst. Rehab. Eng..
Antunes, J., Selvam, A., Bera, K., Brady, J., Willis, J., Paspulati, R., Madabhushi, A., Delaney, C., & Viswanath, S. E.(2018).857 - Machine Learning Analysis of the Whole Rectal Wall on Post-Neoadjuvant Chemoradiation MRI may offer Accurate Identifiction of Rectal Cancer Patients Needing more Aggressive Follow-Up or Surgery.Gastroenterology,154(6).
