Proceedings ArticleDOI
Fast few-shot transfer learning for disease identification from chest x-ray images using autoencoder ensemble
Angshuman Paul,Yuxing Tang,Ronald M. Summers +2 more
- Vol. 11314, pp 33-38
TLDR
A fast few-shot learning framework that uses transfer learning to identify different lung and chest diseases and conditions from chest x-rays, and introduces the idea of autoencoder ensemble to design the classifier.Abstract:
We propose a fast few-shot learning framework that uses transfer learning to identify different lung and chest diseases and conditions from chest x-rays. Our model can be trained with as few as five training examples, making it potentially applicable for diagnosis of rare diseases. In this work, we divide different chest diseases into two disjoint categories: (i) base classes (with large training set) and (ii) novel classes (with a few training examples per class). Our method consists of two steps, namely feature extraction and classification. For the feature extraction, we employ a deep convolutional neural network, customized for chest x-rays. We train the feature extractor with data only from base classes. So the novel classes are unseen to the feature extractor during training. However, we use the feature extractor for extracting features from the data of novel classes resulting in transfer learning. Our classifier, on the other hand, uses the data only from the novel classes for training. We introduce the idea of autoencoder ensemble to design the classifier. Only a few feature vectors from each of the novel classes are used for training the classifier making it a few-shot learner. Incorporating new novel classes require training only the classifier which makes the entire process extremely fast. The performance of the classifier is evaluated on the test data from the novel classes. Experiments show _ 18% improvement in the F1 score compared to the baseline on identifying the novel diseases from publicly available chest x-ray dataset.read more
Citations
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Journal ArticleDOI
Deep learning for chest X-ray analysis: A survey.
TL;DR: In this article, a review of deep learning on chest X-ray images is presented, focusing on image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation.
Posted Content
Learning from Very Few Samples: A Survey
TL;DR: This survey extensively review 300+ papers of FSL spanning from the 2000s to 2019 and provides a timely and comprehensive survey for FSL, which categorize FSL approaches into the generative model based and discriminative modelBased kinds in principle, and emphasize particularly on the meta learning based FSL approach.
Journal ArticleDOI
Discriminative ensemble learning for few-shot chest x-ray diagnosis.
TL;DR: The proposed method for few-shot diagnosis of diseases and conditions from chest x-rays using discriminative ensemble learning is modular and easily adaptable to new tasks requiring the training of only the saliency-based classifier.
Journal ArticleDOI
A Unified Framework for Generalized Low-Shot Medical Image Segmentation With Scarce Data
TL;DR: Zhang et al. as discussed by the authors proposed a unified framework for generalized low-shot (one and few-shot) medical image segmentation based on distance metric learning (DML).
Journal ArticleDOI
Generalized Zero-Shot Chest X-Ray Diagnosis Through Trait-Guided Multi-View Semantic Embedding With Self-Training
Angshuman Paul,Thomas C. Shen,Sungwon Lee,Niranjan Balachandar,Yifan Peng,Zhiyong Lu,Ronald M. Summers +6 more
TL;DR: In this article, a multi-view semantic embedding strategy for generalized zero-shot diagnosis of chest radiographs is proposed, which leverages the potential of multiview semantic embeddings.
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CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
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Journal ArticleDOI
Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.
Daniel Shu Wei Ting,Daniel Shu Wei Ting,Carol Y. Cheung,Carol Y. Cheung,Gilbert Lim,Gavin Tan,Gavin Tan,Nguyen Duc Quang,Alfred Tau Liang Gan,Haslina Hamzah,Renata Garcia-Franco,Ian Yew San Yeo,Ian Yew San Yeo,Shu Yen Lee,Shu Yen Lee,Edmund Yick Mun Wong,Edmund Yick Mun Wong,Charumathi Sabanayagam,Charumathi Sabanayagam,Mani Baskaran,Mani Baskaran,Farah Nur Ilyana Mohd Ibrahim,Ngiap Chuan Tan,Ngiap Chuan Tan,Eric A. Finkelstein,Ecosse L. Lamoureux,Ecosse L. Lamoureux,Ian Y. H. Wong,Neil M. Bressler,Sobha Sivaprasad,Rohit Varma,Jost B. Jonas,Mingguang He,Ching-Yu Cheng,Ching-Yu Cheng,Gemmy Cheung,Gemmy Cheung,Tin Aung,Tin Aung,Wynne Hsu,Mong Li Lee,Tien Yin Wong,Tien Yin Wong +42 more
TL;DR: In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases.