19 research outputs found

    Two-layer ensemble of deep learning models for medical image segmentation. [Article]

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    One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation algorithms can potentially assist physicians with more effective imaging-based diagnoses. However, since it is difficult to acquire high-quality ground truths for medical images and DNN hyperparameters require significant manual tuning, the results by DNN-based medical models might be limited. A potential solution is to combine multiple DNN models using ensemble learning. We propose a two-layer ensemble of deep learning models in which the prediction of each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weight-based scheme which is found by solving linear regression problems. To the best of our knowledge, our paper is the first work which proposes a two-layer ensemble of deep learning models with an augmented data technique in medical image segmentation. Experiments conducted on five different medical image datasets for diverse segmentation tasks show that proposed method achieves better results in terms of several performance metrics compared to some well-known benchmark algorithms. Our proposed two-layer ensemble of deep learning models for segmentation of medical images shows effectiveness compared to several benchmark algorithms. The research can be expanded in several directions like image classification

    Generative Adversarial Network (GAN) for Medical Image Synthesis and Augmentation

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    Medical image processing aided by artificial intelligence (AI) and machine learning (ML) significantly improves medical diagnosis and decision making. However, the difficulty to access well-annotated medical images becomes one of the main constraints on further improving this technology. Generative adversarial network (GAN) is a DNN framework for data synthetization, which provides a practical solution for medical image augmentation and translation. In this study, we first perform a quantitative survey on the published studies on GAN for medical image processing since 2017. Then a novel adaptive cycle-consistent adversarial network (Ad CycleGAN) is proposed. We respectively use a malaria blood cell dataset (19,578 images) and a COVID-19 chest X-ray dataset (2,347 images) to test the new Ad CycleGAN. The quantitative metrics include mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), spatial correlation coefficient (SCC), spectral angle mapper (SAM), visual information fidelity (VIF), Frechet inception distance (FID), and the classification accuracy of the synthetic images. The CycleGAN and variant autoencoder (VAE) are also implemented and evaluated as comparison. The experiment results on malaria blood cell images indicate that the Ad CycleGAN generates more valid images compared to CycleGAN or VAE. The synthetic images by Ad CycleGAN or CycleGAN have better quality than those by VAE. The synthetic images by Ad CycleGAN have the highest accuracy of 99.61%. In the experiment on COVID-19 chest X-ray, the synthetic images by Ad CycleGAN or CycleGAN have higher quality than those generated by variant autoencoder (VAE). However, the synthetic images generated through the homogenous image augmentation process have better quality than those synthesized through the image translation process. The synthetic images by Ad CycleGAN have higher accuracy of 95.31% compared to the accuracy of the images by CycleGAN of 93.75%. In conclusion, the proposed Ad CycleGAN provides a new path to synthesize medical images with desired diagnostic or pathological patterns. It is considered a new approach of conditional GAN with effective control power upon the synthetic image domain. The findings offer a new path to improve the deep neural network performance in medical image processing

    Deep Learning Techniques for Multi-Dimensional Medical Image Analysis

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    Deep Learning Techniques for Multi-Dimensional Medical Image Analysis

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    Ensemble of deep learning models with surrogate-based optimization for medical image segmentation.

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    Deep Neural Networks (DNNs) have created a breakthrough in medical image analysis in recent years. Because clinical applications of automated medical analysis are required to be reliable, robust and accurate, it is necessary to devise effective DNNs based models for medical applications. In this paper, we propose an ensemble framework of DNNs for the problem of medical image segmentation with a note that combining multiple models can obtain better results compared to each constituent one. We introduce an effective combining strategy for individual segmentation models based on swarm intelligence, which is a family of optimization algorithms inspired by biological processes. The problem of expensive computational time of the optimizer during the objective function evaluation is relieved by using a surrogate-based method. We train a surrogate on the objective function information of some populations and then use it to predict the objective values of each candidate in the subsequent populations. Experiments run on a number of public datasets indicate that our framework achieves competitive results within reasonable computation time

    Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning

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    Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults. DR lesions can be challenging to identify in fundus images, and automatic DR detection systems can offer strong clinical value. Of the publicly available labeled datasets for DR, the Indian Diabetic Retinopathy Image Dataset (IDRiD) presents retinal fundus images with pixel-level annotations of four distinct lesions: microaneurysms, hemorrhages, soft exudates and hard exudates. We utilize the HEDNet edge detector to solve a semantic segmentation task on this dataset, and then propose an end-to-end system for pixel-level segmentation of DR lesions by incorporating HEDNet into a Conditional Generative Adversarial Network (cGAN). We design a loss function that adds adversarial loss to segmentation loss. Our experiments show that the addition of the adversarial loss improves the lesion segmentation performance over the baseline.Comment: Accepted to International Conference on Image Analysis and Recognition, ICIAR 2019. Published at https://doi.org/10.1007/978-3-030-27272-2_29 Code: https://github.com/zoujx96/DR-segmentatio

    Deep Semantic Segmentation of Natural and Medical Images: A Review

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    The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups. Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation.Comment: 45 pages, 16 figures. Accepted for publication in Springer Artificial Intelligence Revie
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