Abstract
The extraordinary outcomes of deep learning methods should be viewed in the light of certain prerequisites, the first and foremost being the necessity of large amounts of clean labeled data. For medical imaging, dataset labeling costs are pretty steep given the domain expertise required for labeling medical data. This, in combination with the high computing cost that is associated with deep learning algorithms, prevents the democratization of utilizing machine learning algorithms in low- and middle-income countries. This is especially true for India which is data-rich due to the rapid digitalization of medical imaging procedures but labeled-data poor. In this tutorial, we will demonstrate several robust ML optimization techniques that can be used in low-data, low-resource settings. Our tutorial contains several semi-supervised and weakly supervised methods that utilize shallow machine-learning models and heuristic-driven models as labeling functions to label only a subset of unlabeled data points, instead of the whole set. We will demonstrate how close are these methods to the state-of-the-art deep learning methods, demonstrating in their utility for labeling medical datasets.
List of Topics to be covered
- Introduction: Why is Data Efficient ML required in Medical Field – Dr. Kshitij Jadhav
- DIAGNOSE: Avoiding Out-of-Distribution Data Using Submodular Information Measures- Prof Ganesh Ramakrishnan
- CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification- Dr. Venkatapathy Subramanian
- Ensemble Shallow ML models as Labeling Functions in Semi-Supervised Learning – Dr. Kshitij Jadhav
- Weakly supervised deep learning for whole slide lung cancer image analysis – Dr. Venkatapathy Subramanian
- Conclusion
Speakers
- Dr. Kshitij Jadhav, Assistant Professor, IIT Jodhpur
- Prof. Ganesh Ramakrishnan, Professor, IIT Bombay
- Dr. Venkatapathy Subramanian, Assistant Professor, IIT Jodhpur