Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection

by   Fouzia Altaf, et al.

Annotating medical images for disease detection is often tedious and expensive. Moreover, the available training samples for a given task are generally scarce and imbalanced. These conditions are not conducive for learning effective deep neural models. Hence, it is common to 'transfer' neural networks trained on natural images to the medical image domain. However, this paradigm lacks in performance due to the large domain gap between the natural and medical image data. To address that, we propose a novel concept of Pre-text Representation Transfer (PRT). In contrast to the conventional transfer learning, which fine-tunes a source model after replacing its classification layers, PRT retains the original classification layers and updates the representation layers through an unsupervised pre-text task. The task is performed with (original, not synthetic) medical images, without utilizing any annotations. This enables representation transfer with a large amount of training data. This high-fidelity representation transfer allows us to use the resulting model as a more effective feature extractor. Moreover, we can also subsequently perform the traditional transfer learning with this model. We devise a collaborative representation based classification layer for the case when we leverage the model as a feature extractor. We fuse the output of this layer with the predictions of a model induced with the traditional transfer learning performed over our pre-text transferred model. The utility of our technique for limited and imbalanced data classification problem is demonstrated with an extensive five-fold evaluation for three large-scale models, tested for five different class-imbalance ratios for CT based COVID-19 detection. Our results show a consistent gain over the conventional transfer learning with the proposed method.


Unsupervised Deep Transfer Feature Learning for Medical Image Classification

The accuracy and robustness of image classification with supervised deep...

Transfer Learning with Human Corneal Tissues: An Analysis of Optimal Cut-Off Layer

Transfer learning is a powerful tool to adapt trained neural networks to...

Distant Domain Transfer Learning for Medical Imaging

Medical image processing is one of the most important topics in the fiel...

Neural Networks Regularization Through Representation Learning

Neural network models and deep models are one of the leading and state o...

Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning

Since December of 2019, novel coronavirus disease COVID-19 has spread ar...

How Much Off-The-Shelf Knowledge Is Transferable From Natural Images To Pathology Images?

Deep learning has achieved a great success in natural image classificati...

Bridging the gap between Natural and Medical Images through Deep Colorization

Deep learning has thrived by training on large-scale datasets. However, ...

Please sign up or login with your details

Forgot password? Click here to reset