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  1. Vidya, K., & Kurian, M. (2018). Novel framework for breast cancer classification for retaining computational efficiency and precise diagnosis. Communications Applied Electronics, 7(15), 1-6. Retrieved from

  2. Brassey, C. A., O'Mahoney, T. G., Chamberlain, A. T., & Sellers, W. I. (2017). A volumetric technique for fossil body mass estimation applied to Australopithecus afarensis. Journal of Human Evolution, 115, 47-64. DOI:10.1016/j.jhevol.2017.07.014

  3. Webb, G. (2018). A Caussian mixture model based level set method for volume segmentation in medical images. Retrieved from

  4. Omotosho, A., Oluwatobi, A. E., Oluwaseun, O. R., Chukwuka, A. E., & Adekanmi, A. (2018). A neuro-fuzzy based system for the classification of cells as cancerous or non-cancerous. International Journal of Medical Research & Health Sciences, 7(5), 155-166. Retrieved from

  5. Russell, P., Fountain, K., Wolverton, D., & Ghosh, D. (2018). TCIA pathfinder: An R client for The Cancer Imaging Archive REST API. Cancer Research. DOI:10.1158/0008-5472.CAN-18-0678

  6. Bennett, W., Smith, K., Jarosz, Q., Nolan, T., & Bosch, W. (2018). Reengineering workflow for curation of DICOM datasets. Journal of Digital Imaging, 1-9. DOI:10.1007/s10278-018-0097-4

  7. Yassine, A.-A., Kingsford, W., Xu, Y., Cassidy, J., Lilge, L., & Betz, V. (2018). Automatic interstitial photodynamic therapy planning via convex optimization. Biomedical Optics Express, 9(2), 898-920. DOI:10.1364/BOE.9.000898

  8. Sharma, M., Bhatt, J. S., & Joshi, M. V. (2018). Early detection of lung cancer from CT images: Nodule segmentation and classification using deep learning. Tenth International Conference on Machine Vision. 106960W. Vienna: SPIE. DOI:10.1117/12.2309530
  9. Saad, M., & Choi, T.-S. (2018). Computer-assisted subtyping and prognosis for non-small cell lung cancer patients with unresectable tumor. Computerized Medical Imaging and Graphics, 67, 1-8. DOI:10.1016/j.compmedimag.2018.04.003
  10. Nishio, M., Nishizawa, M., Sugiyama, O., Kojima, R., Yakami, M., Kuroda, T., Togashi, K. (2018). Computer aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization. Plos One, 13(4). DOI:10.1371/journal.pone.0195875
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  12. Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K., Davidson, B., Pereira, S. P., Clarkson, M. J., Barratt, D. C. (2018). Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE Transaction on Medical Imaging. DOI:10.1109/TMI.2018.2806309
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  14. Chacko, L. J., Schmidbauer, D. T., Handschuh, S., Reka, A., Fritscher, K. D., Raudaschl, P., Saba, R., Handler, M., Schier, P. P., Baumgarten, D., Fischer, N., Pechriggl, E. J., Brenner, E., Hoermann, R., Glueckert, R., Schrott-Fischer, A. (2018). Analysis of vestibular labyrinthine geometry and variation in the human temporal bone. Frontiers in Neuroscience, 12. DOI:10.3389/fnins.2018.00107
  15. Causey, J., Zhang, J., Ma, S., Jiang, B., Qualls, J., Politte, D. G., Prior, F., Zhang, S., Huang, X. (2018). Highly accurate model for prediciton of lung nodule malignancy with CT scans. Retrieved from
  16. Gillmann, C., Arbelaez, P., Penaloza, J. T., Hagen, H., & Wischgoll, T. (2017). Intuitive error space exploration of medical image data in clinical daily routineEurographics Conference on Visualization (EuroVis) 2017. DOI:10.2312/eurovisshort.20171148
  17. Jinu, J., Rajesh, K. R., Pournami, S. C., & Vidya, P. (2017). Interactive 3D Virtual Colonoscopic Navigation For Polyp Detection From CT ImagesProcedia Computer Science, 115, 407-414. DOI:10.1016/j.procs.2017.09.099
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  20. Mitra, S., Banerjee, S., & Hayashi, Y. (2017). Volumetric brain tumour detection from MRI using visual saliency. (J. Najbauer, Ed.) PLOS One, 12(11). 10.1371/journal.pone.0187209
  21. Gueziri, H.-E. (2017). User-centered design and evaluation of interactive segmentation methods for medical images. Montreal: École de technologie supérieure du Quebec.  Retrieved from

  22. Lan, R., Zhong, S., Liu, Z., Shi, Z., & Luo, X. (2017). A simple texture feature for retrieval of medical images. Multimedia Tools and Applications.  DOI:10.1007/s11042-017-5341-2

  23. Prior, F., Smith, K., Sharma, A., Kirby, J., Tarbox, L., Clark, K., Bennett, W., Nolan, T., Freymann, J. (2017). The public cancer radiology imaging collections of The Cancer Imaging ArchiveNature Scientific Data, 4; 1-7. DOI:10.1038/sdata.2017.124

  24. Kohli, M., Morrison, J. J., Wawira, J., Morgan, M. B., & Hostetter, J., Genereaux, B., Hussain, M., Langer S. G. (2017). Creation and curation of the society of imaging informatics in medicine hackathon datasetJournal of Digital Imaging, 1-4. DOI:10.1007/s10278-017-0003-5

  25. Williamson, J.F., Das, S.K., Goodsitt, M.S., Deasy, J.O. (2017). Introducing the Medical Physics Dataset Article. Med. Phys. 44(2); 349-350. DOI:10.1002/mp.12003
  26. Nida, N; Khan, M. (2016). Efficient Colorization of Medical Imaging based on Colour Transfer Method. U.G. Proceedings of the Pakistan Academy of Sciences: B. Life and Environmental Sciences, 53(4); 253-261. (link)
  27. Kalpathy-Cramer, J., Zhao, B., Goldgof, D., Gu, Y., Wang, X., Yang, H., Tan, Y., Gillies, R., Napel, S. (2016). A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study. J Digit Imaging29(4):476-487. DOI:10.1007/s10278-016-9859-z
  28. Parks, C.L., Monson, K.L. (2016). Automated Facial Recognition of Computed Tomography-Derived Facial Images: Patient Privacy Implications. Journal of Digital Imaging. 1-11. DOI:10.1007/s10278-016-9932-7

  29. Huang, B.E., Mulyasasmita, W., Rajagopal, G. (2016). The Path from Big Data to Precision Medicine.Expert Review of Precision Medicine and Drug Development,1(2):129-143. (link)

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  31. Benedict, S.H., Hoffman K., Martel, M.K., Abernethy, A.P., Asher, A.L., Capala, J., Chen, R.C., Chera, B., Couch, J., Deye, J., Efstathiou, J.A., Ford, E., Fraass, B.A., Gabriel, P.E., Huser, V., Kavanagh, B.D., Khuntia, D., Marks, L.B., Mayo, C., McNutt, T., Miller, R.S., Moore, K.L., Prior, F., Roelofs, E., Rosenstein, B.S., Sloan, J., Theriault, A., Vikram, B. (2016). Overview of the American Society for Radiation Oncology–National Institutes of Health–American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data.International Journal of Radiation Oncology: Biology, Physics. 95(3):873-879 (link)
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  33. Moore, S.M., Maffitt, D.R., Smith, K.E., Kirby, J.S., Clark, K.W., Freymann, J.B., Vendt, B.A., Tarbox, L.R., Prior, F.W. (2015). De-identification of Medical Images with Retention of Scientific Research Value. RadioGraphics. 35(3); 727-35. DOI:10.1148/rg.2015140244.
  34. Mayo, C.S., Deasy, J.O., Chera, B.S., Freymann, J., Kirby, J.S., Hardenberg, P.H. (2016). How Can We Effect Culture Change Toward Data-Driven Medicine?International Journal of Radiation Oncology: Biology, Physics95(3); 916-21. (link)
  35. Kirby, J., Tarbox, L., Freymann, J., Jaffe, C., Prior, F. (2015). "TU-AB-BRA-03: The Cancer Imaging Archive: Supporting Radiomic and Imaging Genomic Research with Open-Access Data Sets."Medical physics 42(6): 3587-3587.  DOI:10.1118/1.4925508
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  1. Golan, R. (2018). DeepCADe: A deep learning architecture for the detection of lung nodules in CT scans. (link to thesis)

  2. Großmann, P. B. H. J. Defining the biological and clinical basis of radiomics: towards clinical imaging biomarkers. Datawyse / Universitaire Pers Maastricht 2018. DOI: 10.26481/dis.20180308pg (link to thesis)

  3. Webb, G. (2018). A Gaussian mixture model based level set method for volume segmentation in medical images. Linköping, Sweden. (link to thesis)
  4. Androutsou, T. Clinical Decision Support System for Lung Cancer Diagnosis by analysis of thoracic CT images.  Carrier NTUA, Department of Electrical and Computer Engineering 2017. (link to thesis)

  5. Emirzade, Erkan.  A COMPUTER AIDED DIAGNOSIS SYSTEM FOR  LUNG CANCER DETECTION USING SVM. The Graduate School Of Applied Sciences Of Near East University, 2016. (link to thesis)
  6. Yu, Zexi. Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images. University of Saskatchewan 2016. (link to thesis)
  7. Albalooshi FA. Self-organizing Approach to Learn a Level-set Function for Object Segmentation in Complex Background Environments. University of Dayton; 2015. (link to thesis)

  8. Camlica Z. Image Area Reduction for Efficient Medical Image Retrieval. Waterloo, Ontario, Canada,: University of Waterloo; 2015. (link to thesis)

  9. Hunter L. Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction. Thesis, University of Texas; 2013.  (link to thesis)
  10. Karnayana PM. Radiogenomic correlation for prognosis in patients with glioblastoma multiformae. San Diego State University; 2013. (link to thesis)

  11. Nabizadeh, N. Automated Brain Lesion Detection and Segmentation Using Magnetic Resonance Images. Electrical and Computer Engineering. Miami, FL, University of Miami. PhD., 2015. (link to thesis)

  12. Wieser, H.-P.  Supervised Machine Learning Approach Utilizing Artificial Neural Networks for Automated Prostate Zone Segmentation in Abdominal MR images. Klagenfurt, Austria, Fachhochschule Kärnten/Carinthia University of Applied Sciences; 2013.(link to thesis)