Transforming Global Health with Affordable & Inclusive AI

Transforming glObal heAlth with afforDable and incluSive AI (TOADS) is a research group within NAAMII led by Bishesh Khanal. This group focuses on theoretical and applied research in Machine learning (ML) that has the potential to transform global health. The goal is to push the boundaries of ML theory and applications to tackle challenging problems in healthcare with a focus on applications that are directly relevant to Low-Income Countries (LICs). We actively explore potentially unmet clinical needs of resource-constrained regions such as rural areas of Nepal, identify diseases and clinical workflow where AI/ML can have the biggest impact, and prioritize devices or tools that are low cost so that the solutions will be accessible to the poor people who are unable to afford expensive health care costs. Major theoretical topics we are focusing on: Semi-supervised learning, Domain Adaptation, Generative Adversarial Networks, Geometric Deep Learning. Priority areas: Ultrasound, X-rays, smart-phone, EEG, and ECG.

Latest Related Publications

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Bidur Khanal, Lavsen Dahal, Prashant Adhikari, Bishesh Khanal
Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression
Medical Imaging, Computer Vision, Machine Learning, Artificial Intelligence Computational Methods and Clinical Applications for Spine Imaging. MICCAI 2019 CSI Workshop & Challenge, Shenzen, China , 2019
Bibtex

@article{khanal2019automatic,
  title={Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression},
  author={Khanal, Bidur and Dahal, Lavsen and Adhikari, Prashant and Khanal, Bishesh},
  url={arXiv preprint arXiv:1910.14202},
  year={2019},
  maintitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019},
  booktitle = {Computational Methods and Clinical Applications for Spine Imaging. CSI Workshop and Challenge}
}

Alberto Gomez, Veronika Zimmer, Nicolas Toussaint, Robert Wright, JamesR. Clough, Bishesh Khanal, MilouVan Poppel, Emily Skelton, Jackie Matthews, JuliaA. Schnabel
Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging
MLMIR workshop in MICCAI (accepted), 2019
Bibtex

                  @inproceedings{gomez2019image,
                    title={Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging},
                    author={Gomez, Alberto and Zimmer, Veronika and Toussaint, Nicolas and Wright, Robert and Clough, James R. and Khanal, Bishesh and Poppel, Milou Van and Skelton, Emily and Matthews, Jackie and Schnabel, Julia A.},
                    booktitle={MLMIR in MICCAI},
                    year={2019},
		    note={Accepted},                    
                  }

Samuel Budd, Matthew Sinclair, Bishesh Khanal, Jacqueline Matthew, David Llyod, Alberto Gomez, Nicolas Toussaint, Emma Robinson, Bernhard Kainz
Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers
MICCAI 2019 (Accepted, 31% acceptance rate), 2019
Bibtex

@inproceedings{budd2019confident,
  title={Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers},
  author={Budd, Samuel and Sinclair, Matthew and Khanal, Bishesh and  Matthew, Jacqueline and Llyod, David and Gomez, Alberto and Toussaint, Nicolas and Robinson, Emma and Kainz, Bernhard},
  booktitle={MICCAI},
  year={2019},
  note={Accepted}
}

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