Medical imaging technologies using unorthodox artificial intelligence for early disease diagnoses

My research reduces dependence on specialized medical devices, biological and chemical processes and creates new paradigms for low-cost biomarker imaging and clinical diagnoses at the point-of-care. I have demonstrated a series of generative, prediction and classification algorithms for obtaining medical diagnostic information of organs and tissues from photographs captured by mobile phones and cameras. For example:

  1. In collaboration with Brigham and Women’s Hospital in Boston MA, we devised and published a novel “Computational staining” system to digitally stain photographs of unstained tissue biopsies with Haematoxylin and Eosin (H&E) dyes to diagnose cancer [1, 2]. This research also described an automated “Computational destaining” algorithm that can remove dyes and stains from photographs of previously stained tissues, allowing reuse of patient samples. Our method uses neural networks to help physicians provide timely information about the anatomy and structure of the organ and saving time and precious biopsy samples.
  2. In collaboration with Beth Israel Deaconess Medical Center in Boston MA, we investigated use of dark field imaging of capillary bed under the tongue of consenting patients in emergency rooms for diagnosing sepsis (a blood borne bacterial infection). A neural network capable of distinguishing between images from non-septic and septic patients with more than 90% accuracy was reported for the first time [3]. This approach can rapidly stratify and offer rational use of antibiotics and reduce disease burden in hospital emergency rooms and patients.
  3. We successfully predicted signatures associated with fluorescent porphyrin biomarkers (linked with tumors and periodontal diseases) from standard white-light photographs of the mouth, thus reducing the need for fluorescent imaging [4].
  4. We have also communicated research studies reporting automated segmentation of oral diseases from standard photographs [5] by neural networks and correlations with systemic health conditions such as optic nerve abnormalities in patients [6].

Examples listed above describe our contributions to design novel neural networks and processes that can assist physicians and patients by next-generation computational medicine algorithms at the point-of-care which integrate seamlessly into clinical workflows in hospitals all over the world.


  1. High accuracy tumor diagnoses and benchmarking of hematoxylin and eosin stained prostate core biopsy images generated by explainable deep neural networks. 2019.

    Aman Rana, Lowe A, Lithgow M, Horback K, Janovitz T, Da Silva A, Tsai H, Shanmugam V, Yoon H, Shah P

    Under review

  2. Computational histological staining and destaining of prostate core biopsy RGB images with generative adversarial neural networks. 2018. [Abstract] [Full Paper]

    Aman Rana, Yauney G, Lowe A, Shah P*

    (*Senior author supervising research)

    17th IEEE International Conference of Machine Learning and Applications. DOI: 10.1109/ICMLA.2018.00133

  3. Machine learning algorithms for classification of microcirculation images from septic and non-septic patients. 2018. [Abstract] [Full Paper]

    Perikumar Javia, Rana A, Shapiro NI, Shah P*

    (*Senior author supervising research)

    17th IEEE International Conference of Machine Learning and Applications. DOI: 10.1109/ICMLA.2018.00097

  4. Convolutional neural network for combined classification of fluorescent biomarkers and expert annotations using white light images. 2017. [Abstract] [Full Paper]

    Gregory Yauney, Angelino K, Edlund D, Shah P*

    (*Senior author supervising research, Selected for oral presentation)

    17th annual IEEE International Conference on BioInformatics and BioEngineering. DOI: 10.1109/BIBE.2017.00-37

  5. Automated segmentation of gingival diseases from oral images. 2017. [Abstract] [Full Paper]

    Aman Rana, Yauney G, Wong L, Muftu A, Shah P*

    (*Senior author supervising research)

    IEEE-NIH 2017 Special Topics Conference on Healthcare Innovations and Point-of-Care Technologies. DOI: 10.1109/HIC.2017.8227605

  6. Automated process incorporating machine learning segmentation and correlation of oral diseases with systemic health. 2019. [arXiv Preprint]

    Gregory Yauney, Rana A, Javia P, Wong L, Muftu A, Shah P*

    (*Senior author supervising research)

    41st IEEE International Engineering in Medicine and Biology Conference-(Accepted for publication)

Other MIT Research Areas