• Corpus ID: 244714756

Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion

@article{Thomas2021EnhancedTL,
  title={Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion},
  author={Spencer Angus Thomas},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.14388}
}
  • S. Thomas
  • Published 29 November 2021
  • Computer Science
  • ArXiv
In this work we examine the performance enhancement in classification of medical imaging data when image features are combined with associated non-image data. We compare the performance of eight state-of-the-art deep neural networks in classification tasks when using only image features, compared to when these are combined with patient metadata. We utilise transfer learning with networks pretrained on ImageNet used directly as feature extractors and fine tuned on the target domain. Our… 
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References

SHOWING 1-10 OF 34 REFERENCES
Combining Image Features and Patient Metadata to Enhance Transfer Learning
  • S. Thomas
  • Computer Science
    2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
  • 2021
TLDR
This work uses transfer learning from networks pretrained on ImageNet to extract image features from the ISIC HAM10000 dataset prior to classification, and shows an overall enhancement in performance as assessed by all metrics, only noting degradation in a vgg16 architecture.
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
TLDR
This paper considered four distinct medical imaging applications in three specialties involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner.
An investigation of aggregated transfer learning for classification in digital pathology
TLDR
It is apparent that fine-tuning earlier VGG19 convolutional blocks with breast cancer patches and applying bottleneck feature extraction to soft tissue sarcoma can have an adverse effect on accuracy and other performance measures, Nevertheless, the aggregated approach is a promising method for digital pathology and requires much more investigation.
Can ImageNet feature maps be applied to small histopathological datasets for the classification of breast cancer metastatic tissue in whole slide images?
TLDR
This study demonstrated that for the small dataset, the best pretrained feature extractor was DenseNet201, whereas the best model for training was a fully connected softmax layer with a reported accuracy of 88.20% and an average f1-score of 0.881.
Deep learning for tumor classification in imaging mass spectrometry
TLDR
An adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis are proposed.
A survey on deep learning in medical image analysis
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
TLDR
Two specific computer-aided detection problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification are studied, achieving the state-of-the-art performance on the mediastinal LN detection, and the first five-fold cross-validation classification results are reported.
Hello World Deep Learning in Medical Imaging
TLDR
This tutorial provides a high-level overview of how to build a deep neural network for medical image classification, and provides code that can help those new to the field begin their informatics projects.
Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs
TLDR
This work investigates various transfer learning approaches by leveraging model parameters pre-trained on ImageNet with finetuning on melanoma detection to compare EfficientNet, MnasNet, MobileNet, DenseNet, SqueezeNet, ShuffleNet, Google net, ResNet, ResNeXt, VGG and a simple CNN with and without transfer learning.
Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features
TLDR
The framework proposed is a simple, efficient and effective system for histopathology image automatic analysis that successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathological images with little training data.
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