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Pre-trained models for Developmental Neuroscience

Contact

Mayukh deb 
Bradly Alicea

Deliverables

The top priorities of this proposal are:

  • Train a deep learning model(s) from the image dataset(s) provided. 

  • In the process of training, develop a data augmentation pipeline which can be used on the cellular image datasets (even on the cellular images which are not involved in this project) to help build a model robust enough for its purpose. 

  • Make the trained model portable so that it can be easily integrated into a GUI backend.

About
Recent advances in the field of computer vision and deep learning has shown great promise in their ability to decipher images and derive inferences involving classifications, detection of objects and approximation of certain values with high accuracy. 

Pre-trained models like YOLONet and ResNet are now being used in various industries where they help make our lives easier. But these kinds of models are not yet being used for microscopic images on a large scale. With the right model architecture and training approaches, it is possible to get pre-trained models which would help in the research efforts of many. These pre-trained models, combined with a GUI would act as a community tool which would help speed up the classification of thousands of microscopic images and gain inferences from them.

The top priorities of this proposal are:

  • Train a deep learning model(s) from the image dataset(s) provided. 

  • In the process of training, develop a data augmentation pipeline which can be used on the cellular image datasets (even on the cellular images which are not involved in this project) to help build a model robust enough for its purpose. 

  • Make the trained model portable so that it can be easily integrated into a GUI backend.