Dimitrios E. Diamantis

Dimitrios E. Diamantis, Panagiota Gatoula, Anastasios Koulaouzidis et al.

Medical image synthesis has emerged as a promising solution to address the limited availability of annotated medical data needed for training machine learning algorithms in the context of image-based Clinical Decision Support (CDS) systems. To this end, Generative Adversarial Networks (GANs) have been mainly applied to support the algorithm training process by generating synthetic images for data augmentation. However, in the field of Wireless Capsule Endoscopy (WCE), the limited content diversity and size of existing publicly available annotated datasets, adversely affect both the training stability and synthesis performance of GANs. Aiming to a viable solution for WCE image synthesis, a novel Variational Autoencoder architecture is proposed, namely "This Intestine Does not Exist" (TIDE). The proposed architecture comprises multiscale feature extraction convolutional blocks and residual connections, which enable the generation of high-quality and diverse datasets even with a limited number of training images. Contrary to the current approaches, which are oriented towards the augmentation of the available datasets, this study demonstrates that using TIDE, real WCE datasets can be fully substituted by artificially generated ones, without compromising classification performance. Furthermore, qualitative and user evaluation studies by experienced WCE specialists, validate from a medical viewpoint that both the normal and abnormal WCE images synthesized by TIDE are sufficiently realistic.

Eirini Cholopoulou, Dimitrios E. Diamantis, Dimitra-Christina C. Koutsiou et al.

Effective shadow removal is pivotal in enhancing the visual quality of images in various applications, ranging from computer vision to digital photography. During the last decades physics and machine learning -based methodologies have been proposed; however, most of them have limited capacity in capturing complex shadow patterns due to restrictive model assumptions, neglecting the fact that shadows usually appear at different scales. Also, current datasets used for benchmarking shadow removal are composed of a limited number of images with simple scenes containing mainly uniform shadows cast by single objects, whereas only a few of them include both manual shadow annotations and paired shadow-free images. Aiming to address all these limitations in the context of natural scene imaging, including urban environments with complex scenes, the contribution of this study is twofold: a) it proposes a novel deep learning architecture, named Soft-Hard Attention U-net (SHAU), focusing on multiscale shadow removal; b) it provides a novel synthetic dataset, named Multiscale Shadow Removal Dataset (MSRD), containing complex shadow patterns of multiple scales, aiming to serve as a privacy-preserving dataset for a more comprehensive benchmarking of future shadow removal methodologies. Key architectural components of SHAU are the soft and hard attention modules, which along with multiscale feature extraction blocks enable effective shadow removal of different scales and intensities. The results demonstrate the effectiveness of SHAU over the relevant state-of-the-art shadow removal methods across various benchmark datasets, improving the Peak Signal-to-Noise Ratio and Root Mean Square Error for the shadow area by 25.1% and 61.3%, respectively.

Dimitrios E. Diamantis, Dimitris K. Iakovidis

Gastrointestinal (GI) imaging via Wireless Capsule Endoscopy (WCE) generates a large number of images requiring manual screening. Deep learning-based Clinical Decision Support (CDS) systems can assist screening, yet their performance relies on the existence of large, diverse, training medical datasets. However, the scarcity of such data, due to privacy constraints and annotation costs, hinders CDS development. Generative machine learning offers a viable solution to combat this limitation. While current Synthetic Data Generation (SDG) methods, such as Generative Adversarial Networks and Variational Autoencoders have been explored, they often face challenges with training stability and capturing sufficient visual diversity, especially when synthesizing abnormal findings. This work introduces a novel VAE-based methodology for medical image synthesis and presents its application for the generation of WCE images. The novel contributions of this work include a) multiscale extension of the Vector Quantized VAE model, named as Multiscale Vector Quantized Variational Autoencoder (MSVQ-VAE); b) unlike other VAE-based SDG models for WCE image generation, MSVQ-VAE is used to seamlessly introduce abnormalities into normal WCE images; c) it enables conditional generation of synthetic images, enabling the introduction of different types of abnormalities into the normal WCE images; d) it performs experiments with a variety of abnormality types, including polyps, vascular and inflammatory conditions. The utility of the generated images for CDS is assessed via image classification. Comparative experiments demonstrate that training a CDS classifier using the abnormal images generated by the proposed methodology yield comparable results with a classifier trained with only real data. The generality of the proposed methodology promises its applicability to various domains related to medical multimedia.

Panagiota Gatoula, Dimitrios E. Diamantis, Anastasios Koulaouzidis et al.

Synthetic Data Generation (SDG) based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered by overcoming privacy barriers that currently render clinical data sharing difficult. This is the key to accelerating the development of digital tools contributing to enhanced patient safety. Such tools include robust data-driven clinical decision support systems, and example-based digital training tools that will enable healthcare professionals to improve their diagnostic performance for enhanced patient safety. This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images. Its scientific contributions include a) a novel protocol for the systematic Clinical Evaluation of Medical Image Synthesis (CEMIS); b) a novel variational autoencoder-based model for the generation of high-resolution synthetic WCE images; and c) a comprehensive evaluation of the synthetic images using the CEMIS protocol by 10 international WCE specialists, in terms of image quality, diversity, and realism, as well as their utility for clinical decision-making. The results show that TIDE-II generates clinically plausible, very realistic WCE images, of improved quality compared to relevant state-of-the-art generative models. Concludingly, CEMIS can serve as a reference for future research on medical image-generation techniques, while the adaptation/extension of the architecture of TIDE-II to other imaging domains can be promising.

Dimitrios E. Diamantis, Dimitris K. Iakovidis

Convolutional Neural Networks (CNNs) are artificial learning systems typically based on two operations: convolution, which implements feature extraction through filtering, and pooling, which implements dimensionality reduction. The impact of pooling in the classification performance of the CNNs has been highlighted in several previous works, and a variety of alternative pooling operators have been proposed. However, only a few of them tackle with the uncertainty that is naturally propagated from the input layer to the feature maps of the hidden layers through convolutions. In this paper we present a novel pooling operation based on (type-1) fuzzy sets to cope with the local imprecision of the feature maps, and we investigate its performance in the context of image classification. Fuzzy pooling is performed by fuzzification, aggregation and defuzzification of feature map neighborhoods. It is used for the construction of a fuzzy pooling layer that can be applied as a drop-in replacement of the current, crisp, pooling layers of CNN architectures. Several experiments using publicly available datasets show that the proposed approach can enhance the classification performance of a CNN. A comparative evaluation shows that it outperforms state-of-the-art pooling approaches.