487 research outputs found
Synthesizing skin lesion images using CycleGANs – a case study
Generative adversarial networks (GANs) have seen some success as a way to synthesize training data for supervised machine learning models. In this work, we design two novel approaches for synthetic image generation based on CycleGANs, aimed at generating realistic-looking, class-specific dermoscopic skin lesion images. We evaluate the images’ usefulness as additional training data for a convolutional neural network trained to perform a difficult lesion classification task. We are able to generate visually striking images, but their value for augmenting the classifier’s training data set is low. This is in-line with other researcher’s investigations into similar GAN models, indicating the need for further research into forcing GAN models to produce samples further from the training data distribution, and to find ways of guiding the image generation using feedback from the ultimate classification objective
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Synthetic Sample Selection via Reinforcement Learning
Synthesizing realistic medical images provides a feasible solution to the
shortage of training data in deep learning based medical image recognition
systems. However, the quality control of synthetic images for data augmentation
purposes is under-investigated, and some of the generated images are not
realistic and may contain misleading features that distort data distribution
when mixed with real images. Thus, the effectiveness of those synthetic images
in medical image recognition systems cannot be guaranteed when they are being
added randomly without quality assurance. In this work, we propose a
reinforcement learning (RL) based synthetic sample selection method that learns
to choose synthetic images containing reliable and informative features. A
transformer based controller is trained via proximal policy optimization (PPO)
using the validation classification accuracy as the reward. The selected images
are mixed with the original training data for improved training of image
recognition systems. To validate our method, we take the pathology image
recognition as an example and conduct extensive experiments on two
histopathology image datasets. In experiments on a cervical dataset and a lymph
node dataset, the image classification performance is improved by 8.1% and
2.3%, respectively, when utilizing high-quality synthetic images selected by
our RL framework. Our proposed synthetic sample selection method is general and
has great potential to boost the performance of various medical image
recognition systems given limited annotation.Comment: MICCAI202
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