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AiMed Lab
Indiana University Indianapolis
AIMed Lab

List of Publications

Explore our published research contributions to the fields of educational technology, human-computer interaction, and artificial intelligence.

2026

  • Sonawane S, Sompalle P, Baddula D, Kedare M, Setiadi N, Midya A, Azamat S, Zhang Z, Patil D, Yang A, Kikano E, Hill D, Massuti T, Sanda M, Madabhushi A, Bahler C, Shiradkar R.Enhancing Robustness of Deep Learning to Batch Effects from Multi-Site Data for Segmentation of Clinically Significant Prostate Cancer on MRI. Journal of Imaging Informatics in Medicine Accepted for publication. Read Paper
  • Bogale, Y., Collins, K., Feldman, M., Bahler, C., and Shiradkar, R., "Foundation-model-based prostate cancer segmentation on whole mount digitized H&E radical prostatectomy section", Proceedings of SPIE Medical Imaging, 2026.
    Read Paper
  • Shah, A., Bogale, Y., Murugan, N., Nair, V., Collins, K., Bahler, C., Feldman, M., and Shiradkar, R., "Deep-learning-based segmentation of glands and stroma on digitized H&E-stained prostate whole slide images", Proceedings of SPIE Medical Imaging.  
  • Sonawane, S. A., Sompalle, P., Yang, A., Oderinde, O., Bahler, C., Hill, D., and Shiradkar, R., "Impact of site-specific batch effects on deep learning for prostate cancer lesion segmentation on MRI", Proceedings of SPIE Medical Imaging.
    Read Paper

2025

  • Bilen MA, Vo BT, Liu Y, Greenwald R, Davarpanah AH, McGuire D, Shiradkar R, Li L, Midya A, Nazha B, Brown JT, Williams S, Session W, Russler G, Caulfield S, Joshi SS, Narayan VM, Filson CP, Ogan K, Kucuk O, Carthon BC, Del Balzo L, Cohen A, Boyanton A, Prokhnevska N, Cardenas MA, Sobierajska E, Jansen CS, Patil DH, Nicaise E, Osunkoya AO, Kissick HT, Master VA., “Neoadjuvant cabozantinib for locally advanced nonmetastatic clear cell renal cell carcinoma: a phase 2 trial”. Nature Cancer. 2025 Mar 1;6(3):432–444. Read Paper
  • Guha A, Shah V, Nahle T, Singh S, Kunhiraman HH, Shehnaz F, Nain P, Makram OM, Mahmoudi M, Al-Kindi S, Madabhushi A, Shiradkar R, Daoud H. “Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review”. Curr Cardiol Rep.  Read Paper
  • Midya A, Tirumani S, Bittencourt LK, Azamat S, Balakrishnan S, Hiremath A, Wido S, Fu P, Ponsky L, Madabhushi A, Shiradkar R., “Population-Specific Radiomics From Biparametric Magnetic Resonance Imaging Improves Prostate Cancer Risk Stratification in African American Men”. JU Open Plus.  Read Paper
  • Zhang Z, Yang Q, Shiradkar R, Mirtti T, Azamat S, Xuan K, Xu J, Madabhushi A. “A deep learning derived prostate zonal volume-based biomarker from T2-weighted MRI to distinguish between prostate cancer and benign prostatic hyperplasia”. Med Phys.  Read Paper
  • Anuyah S., Kaushik MM, Dwarampudi SRKR, Shiradkar R, Durresi A., Chakraborty S., “Automated knowledge graph construction using large language models and sentence complexity modelling,” Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025, pp. 15526–15550. Read Paper

2024

  • Hiremath, A., Corredor, G., Li, L., Leo, P., Magi-Galluzzi, C., Elliott, R., Purysko, A., Shiradkar, R., Madabhushi, A., “An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings”, Cell Heliyon, 10(8). Read Paper
  • Hiremath, A., Viswanathan, V. S., Bera, K., Shiradkar, R., Yuan, L., Armitage, K., Gilkeson, R., Ji, M., Fu, P., Gupta, A., Lu, C., & Madabhushi, A., “Deep Learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study. Computers in Biology and Medicine”, Computers in Biology and Medicine. Read Paper
  • Lakshya Seth , Omar Makram , Amr Essa , Vraj Patel , Stephanie Jiang , Aditya Bhave ,Sandeep Yerraguntla , Gaurav Gopu , Sarah Malik , Justin Swaby , Johnathon Rast , Caleb A. Padgett, Ahmed Shetewi , Priyanshu Nain , Neal Weintraub , Eric D. Miller , Susan Dent , Ana Barac , Rakesh Shiradkar , Anant Madabhushi , Catherine Ferguson , Avirup Guha, “Laterality of Radiation Therapy in Breast Cancer is Not Associated with Increased Risk for Coronary Artery Disease in the Contemporary Era”, Advances in Radiation Oncology. Read Paper

2023

  • Li, L., Shiradkar, R., Tirumani, S., Bittencourt, L.K., Fu, P., Mahran, A., Buzzy, C., Stricker, P.D., Rastinehad, A.R., Magi-Galluzzi, C., Ponsky, L., Klein, E., Purysko, A.S., Madabhushi, A., “Novel Radiomic Analysis on Bi-parametric MRI for Characterizing differences between MR Non-visible and Visible Clinically Significant Prostate Cancer”, European Journal of Radiology Open. Read Paper
  • Midya, A., Hiremath, A., Huber, J., Viswanathan, V. S., Lima, D. O., Mahran, A., Bittencourt, L. K., Tirumani, S. H., Ponsky, L., Madabhushi, A., and Shiradkar, R., “Delta radiomic patterns on serial bi-parametric MRI are associated with pathologic upgrading in prostate cancer patients on active surveillance: Preliminary Findings”, Frontiers in Oncology 2023. Read Paper
  • Banerjee, I., Bhattacharjee, K., Burns, J. L., Trivedi, H., Purkayastha, S., Seyyed-Kalantari, L., Patel, B. N., Shiradkar, R., Gichoya, J. W., “Shortcuts” causing bias in radiology artificial intelligence: causes, evaluation and mitigation” , Journal of the American College of Radiology. Read Paper
  • Li, L., Shiradkar, R., Gottlieb, N., Buzzy, C., Hiremath, A., Viswanathan, V. S., MacLennan, G. T., Lima, D. O., Gupta, K., Lee, D., Tirumani, S. H., Magi-Galluzzi, C., Purysk, A., and Madabhushi, A., “Multi-Scale Statistical Deformation Based Co-registration of Prostate MRI and Post-surgical Whole Mount Histopathology”, Medical Physics. Read Paper
  • O. O., Owosela, Steinberg, R.S., Leslie, S.L., Celi, L., Purkayastha, S., Shiradkar, R., Newsome, J.N., Gichoya, J.W., “Identifying and improving the “ground truth” of race in disparities research through improved EMR data reporting. A systematic review”, International Journal of Medical Informatics, 182:105303. Read Paper

2022

  • Cuocolo, R., Stanzione, A., Castaldo, A., De Lucia, D. R., Imbriaco, M., & Brancato, V., "Artificial Intelligence in Prostate Cancer Imaging: Past, Present, and Future", MDPI. Read Paper
  • Shiradkar R, Ghose S, Mahran A, Li L, Hubbard I, Fu P, Tirumani SH, Ponsky L, Purysko A and Madabhushi A, “ Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings”. Front. Oncol. 12:841801, 2022. Read Paper
  • Shiradkar R. and Chicco D., “Ten quick tips for computational analysis of medical images”,PLOS Computational Biology, 2022. Read Paper
  • Roge, A., Hiremath, A., Sobota, M., Tirumani, S. H., Bittencourt, L. K., Ream, J., Ward, R., Olaniyan, H., Verma, S., Purysko, A., Madabhushi, A., and Shiradkar, R., "Evaluating the sensitivity of deep learning to human reader based lesion delineations in identifying clinically significant prostate cancer on MRI", Proceedings of SPIE Medical Imaging.

2021

  • Hiremath, A., Shiradkar, R., Mahran, A., Rastinehad, A., Tewari, A., Tirumani, S., H., Purysko, A., Ponsky, L., and Madabhushi, A., “An integrated deep learning, PI-RADS and clinical nomogram for identifying clinically significant prostate cancer on bi-parametric MRI: A multi-center study”, Lancet Digital Health. Read Paper
  • Leo, P., Janowczyk, A., Elliott, R., Janaki, N., Bera K., Shiradkar, R., Farre´, X., Fu, P., El-Fahmawi, A., Shahait, M., Kim, J., Lee, D., Yamoah, K., Rebbeck, T.R., Khani, F., Robinson, B.D., Eklund, L., Jambor, I., Merisaari, H., Ettala, O., Taimen, P., Aronen, H.J., Bostro¨m, P.J., Tewari, A., Magi-Galluzzi, C., Klein, E., Purysko, A.S., Shih, N.NC., Feldman, M., Gupta, S., Lal, P., Madabhushi, A., “Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study”. Nature Precision Oncology. Read Paper
  • Lu.C.*, Shiradkar.R.*, Liu,Z., “Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review”, Chinese Journal of Cancer Research. Read Paper
  • Shiradkar R. Editorial for "Multiparametric MRI-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension with Prostate Cancer". J Magn Reson Imaging. 
  • Shiradkar, R. Editorial for "Multiparametric MRI-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension with Prostate Cancer". J Magn Reson Imaging. 2021 May 28. doi: 10.1002/jmri.27747. PMID: 34050577. Read Paper
  • Hiremath, A., Yuan, L., Shiradkar, R., Bera, K., Viswanathan VS, Vaidya, P., Furin, J., Armitage, K., Gilkeson, R., Ji, M., Fu P., Gupta, A., Lu, C., and Madabhushi, A., “LuMiRa: An Integrated Lung Deformation Atlas and 3D-CNN model of Infiltrates for COVID-19 Prognosis”, Medical Image Computing and Computer Assisted Interventions (MICCAI. Read Paper

2020

  • Algohary, A., Shiradkar, R., Pahwa, S., Purysko, A., Verma, S., Moses, D., Shnier, R., Haynes, A., Delprado, W., Thompson, J., Tirumani, S., Mahran, A., Rastinehad, A. R., Ponsky, L., Stricker, P. D., & Madabhushi, A., "Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site StudyDefault value for title", MDPI. Read Paper
  • Hiremath A, Shiradkar R, Merisaari H, Prasanna P, Ettala O, Taimen P, Aronen HJ, Bostrom P, Jambor I, Madabhushi A. "Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps". European Radiology.  Read Paper
  • Shiradkar, R ., Panda, A., Pahwa, S., Li, L., Leo, P., Janaki, N., Ponsky, L., Elliott, R., Gulani, G., and Madabhushi, A., “T1, T2 MR Fingerprinting Measurements of Prostate Cancer and Prostatitis Correlate with Deep Learning Derived Estimates of Epithelium, Lumen and Stromal Composition on Corresponding Whole Mount Histopathology”, European Radiology.  Read Paper
  • Algohary A., Shiradkar R. , Pahwa S., Purysko A., Verma A., Moses A., Shnier R., Haynes A., Thompson J., Tirumani S., Mahran A., Rastinehad A., Ponsky L., Stricker P., Stricker P. and Madabhushi A., “Combination of Peri-tumoral and Intra-tumoral Radiomic Features on Bi-parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-site Study”, Cancers.  Read Paper
  • Li, L., Shiradkar, R ., Leo, P., Algohary, A., Fu, P., Tirumani, S., Mahran, A., Buzzy, C., Obmann, V.C., Mansoori, B., El-Fahmawi, A., Shahait, M., Shah, A., Tewari, A., Magi-Galluzzi, C., Lee, D., Lal, P., Ponsky, L., Klein, E., Purysko, A.S., Madabhushi, A., “A Novel Imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI”, Lancet EBioMedicine.  Read Paper
  • Shiradkar, R., Zuo, R., Mahran, A., Ponsky, L., Tirumani, S. H., Madabhushi, A., “Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: Preliminary Results”, SPIE Medical Imaging.  Read Paper
  • Hiremath, A., Shiradkar, R., Merisaari, H., Braman, N., Prasanna, P., Ettala, O., Taimen, P., Aronen, H. J., Bostrom, P. J., Jambor, I., Purysko, A., Madabhushi, A., “A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer”, Proceedings of SPIE Medical Imaging, 2020. Read Paper

2019

  • Schelb, P., Kohl, S., Radtke, J. P., Wiesenfarth, M., Kickingereder, P., Bickelhaupt, S., Fechter, T., Yaqubi, K., Kuder, T. A., Eble, M. J., Schlemmer, H. P., Maier-Hein, K. H., & Bonekamp, D., "Automated Detection of Prostate Cancer in Multiparametric MRI Using Deep Learning", DOI. Read Paper
  • Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Silva, V. W. K., Busam, K. J., Brogi, E., & Fuchs, T. J., "Deep Learning for Prostate Cancer Detection and Gleason Scoring Using Biopsy and Multiparametric MRI Data", DOI. Read Paper
  • Merisaari, H., Shiradkar, R., Taimen, P., Ettala, O., Pesola, M., Saunavaara, J., Bostrom, P. J., Madabhushi, A., Aronen, H., Jambor, I., “Repeatability of radiomics and machine learning for Diffusion Weighted Imaging: Short-term repeatability study of 112 patients with prostate cancer”, Magnetic Resonance in Medicine (MRM). Read Paper

2018

  • Armato, S. G., Huisman, H., Madabhushi, A., & Rosen, M., "Learning from Multi-Institutional Data for Prostate MRI Segmentation and Clinical Outcome Prediction", SPIE Digital Library .  Read Paper
  • Algohary, A., Viswanath, S., Shiradkar, R., Ghose, S., Pahwa, S., Moses, D., Jambor, I., Shnier, R., Bohm, M., Haynes, A., Brenner, P., Deprado, W., Thompson, J., Pulbrock, M., Purysko, A., Verma, S., Ponsky, L., Stricker, P., Madabhushi, A., “Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary Findings”, J. Magn.  Reson. Imaging (JMRI).  Read Paper
  • Shiradkar, R., Ghose, S., Jambor, I., Taimen, P., Ettala, O., Purysko, A. S. and Madabhushi, A., “Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings. J. Magn. Reson. Imaging (JMRI).  Read Paper
  • Li, L., Shiradkar, R., Algohary, A., Leo, P., Magi-Galluzzi, C., Klein, E., Purysko, A., Madabhushi, A., “Radiomic Features derived from Pre-Operative Multi-parametric MRI of the Prostate are associated with Decipher Risk Score”, SPIE Medical Imaging. Read Paper

2017

  • Ghose, S., Shiradkar, R., Rusu, M., Mitra, J., Thawani, R., Feldman, M., Gupta, A., Purysko, A.S., Ponsky, L., Madabhushi, A. “Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings”,  Nature Scientific Reports, 7: 15829.  Read Paper

  • Ghose, S., Shiradkar, R., Purysko, A. S., Madabhushi, A., “Field effect induced organ distension (FOrge) Features Predicting Biochemical Recurrence from Pre-treatment Prostate MRI”, in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 442-449. Read Paper

2016

  • Shiradkar, R., Podder, T.K., Algohary, A., Viswanath, S., Ellis, R.J., Madabhushi, A., "Radiomics based Targeted Radiotherapy Planning (Rad-TRaP): A computational framework for prostate cancer treatment planning with MRI", BMC Radiation Oncology, 11(1):148. Read Paper

2014

  • Shiradkar, R. , Tan, P., Ong, S. H., “Auto-calibrating photometric stereo using ring light constraints”, Machine Vision and Applications, Volume 25, Issue 3, pp 801-809.  Read Paper

  • Shiradkar, R.; Li Shen; Landon, G.; Ong, S.H.; Ping Tan, "A New Perspective on Material Classification and Ink Identification," 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2275-2282, 23-28. Read Paper

2013

  • Shiradkar, R., Sim Heng, O., “Surface reconstruction using isocontours of constant depth and gradient,” in Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 360-363. Read Paper