Multi-Stage Transfer Learning System with Lightweight Architectures in Medical Image Classification
Presentation Type
Poster
Student
Yes
Track
Health Care Application
Abstract
Transfer Learning is currently popular in Medical Image classification. Transfer Learning methods are extensively applied with CNN’s such as Res-net, Densenet, VGG16, Inception, etc. for various medical diagnoses. However, these models are computationally expensive and over parameterized. Another challenge we identified is limited labeled datasets are available in the medical image domain preventing the major advancements in Transfer Learning for Medical image classification. We propose a Multi-Stage Transfer Learning System using Lightweight Architecture to tackle limited target dataset problem with quicker training time. Preliminary results suggest that our model performed well on CT Head images by improving the accuracy over traditional single-stage transfer learning.
Start Date
2-11-2020 1:00 PM
Multi-Stage Transfer Learning System with Lightweight Architectures in Medical Image Classification
Volstorff A
Transfer Learning is currently popular in Medical Image classification. Transfer Learning methods are extensively applied with CNN’s such as Res-net, Densenet, VGG16, Inception, etc. for various medical diagnoses. However, these models are computationally expensive and over parameterized. Another challenge we identified is limited labeled datasets are available in the medical image domain preventing the major advancements in Transfer Learning for Medical image classification. We propose a Multi-Stage Transfer Learning System using Lightweight Architecture to tackle limited target dataset problem with quicker training time. Preliminary results suggest that our model performed well on CT Head images by improving the accuracy over traditional single-stage transfer learning.