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Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics
Catherine Bouchard1,2, Renaud Bernatchez1,2, Flavie Lavoie-Cardinal1,2,3
1CERVO Brain Research Centre, Québec, Québec, Canada.
Neurophotonics
|August 28, 2023
Summary
Machine learning in neurophotonics faces data scarcity. Strategies like weakly supervised learning and active learning help analyze complex bioimaging data with limited annotations, improving insights from small datasets.
Area of Science:
- Neurophotonics
- Machine Learning
- Bioimaging
Background:
- Machine learning (ML) accelerates data processing and feature extraction in neurophotonics.
- ML methods are crucial for automated detection and classification in complex neuroimaging datasets.
- A significant challenge in neurophotonics is the scarcity of labeled data, hindering ML algorithm performance.
Purpose of the Study:
- To provide an overview of strategies addressing labeled data scarcity in neurophotonics.
- To discuss the strengths, limitations, and applications of these strategies in bioimaging.
- To highlight the potential of combining different methods for enhanced ML model performance.
Main Methods:
- Weakly supervised learning
- Active learning
- Domain adaptation
Main Results:
- These strategies offer solutions for limited annotated image data in ML.
- Different approaches can be combined to boost model performance.
- Improved accessibility of ML-based analysis for neurophotonics researchers.
Conclusions:
- Addressing data scarcity is key to unlocking ML potential in neurophotonics.
- Flexible ML strategies enable deeper insights from limited bioimaging datasets.
- This work facilitates broader adoption of advanced ML techniques in the field.
