Medical Imaging

Use of Machine Learning for challenges in medical imaging.

This project spans multiple modalities of medical imaging and multiple types of modelling to empower medical experts.

Alzheimer’s Classification - learning predictive classification models for diffusion MRI data to provide decision support for degenerative brain diseases using Deep Neural Network methods currently only used for 2D image classification. This domain is challenging due to the 3D structure of the data as well as the non-visual properties which do not necessarily carry over from other domains.

Digital Pathology - In this dataset we have looked at NLP methods for analyzing medical reports, Deep Learning methods for classifying images, and Manifold Learning methods to extract compact embeddings for using in classifiers and search engines.

Our Papers on Medical Imaging

  1. QQE
    Quantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distribution
    Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
    Machine Learning with Applications (MLWA). 6, 2021.
  2. NLP-DigiPath
    Analysis of Language Embeddings for Classification of Unstructured Pathology Reports
    Aishwarya Krishna Allada, Yuanxin Wang, Veni Jindal, Morteza Babaie, H.R. Tizhoosh, and Mark Crowley
    In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) IEEE, nov, 2021.
  3. Magnification Generalization for Histopathology Image Embedding
    Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, and H. R. Tizhoosh.
    In IEEE International Symposium on Biomedical Imaging (ISBI) apr, 2021.
  4. Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
    Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, and H. R. Tizhoosh.
    In 25th International Conference on Pattern Recognition (ICPR) IEEE, Milan, Italy (virtual), jan, 2021.
  5. Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
    Milad Sikaroudi, Benyamin Ghojogh, Amir Safarpoor, Fakhri Karray, Mark Crowley, and H. R. Tizhoosh.
    In 15th International Symposium on Visual Computing (ISCV 2020) Springer International Publishing, (virtual), oct, 2020.
  6. QQE
    Quantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distribution
    Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
    Machine Learning with Applications (MLWA). 6, 2021.
  7. Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
    Milad Sikaroudi, Amir Safarpoor, Benyamin Ghojogh, Sobhan Shafiei, Mark Crowley, and HR Tizhoosh.
    In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’20) Montreal, Quebec, Canada (virtual), jul, 2020.
  8. Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
    Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei, H.R. Tizhoosh, Fakhri Karray, and Mark Crowley
    In IEEE International Joint Conference on Neural Networks (IJCNN) Glasgow, UK (virtual), jul, 2020.
  9. Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
    Benyamin Ghojogh, Milad Sikaroudi, H.R. Tizhoosh, Fakhri Karray, and Mark Crowley
    In International Conference on Image Analysis and Recognition (ICIAR-2020) Springer, Póvoa de Varzim, Portugal (virtual), jun, 2020.
  10. Application of probabilistically-weighted graphs to image-based diagnosis of Alzheimer’s disease using diffusion MRI
    Syeda Maryam, Laura McCrackin, Mark Crowley, Yogesh Rathi, and Oleg Michailovich.
    In Proceedings of SPIE 101324, Medical Imaging 2017 : Computer-Aided Diagnosis International Society for Optics and Photonics, mar, 2017.