Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance. We propose an active learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are then used within a novel class aware generative adversarial network (CAGAN) to generate realistic chest xray images for data augmentation by transferring characteristics from one class label to another. Experiments show our proposed AL framework is able to achieve state-of-the-art performance by using about $35\%$ of the full dataset, thus saving significant time and effort over conventional methods

A deep learning model to assess and enhance eye fundus image quality

Engineering aims to design, build, and implement solutions that will increase and/or improve the life quality of human beings. Likewise, from medicine, solutions are generated for the same purposes, enabling these two knowledge areas to converge for a common goal. With the thesis work “A Deep Learning Model to Assess and Enhance Eye Fundus Image Quality", a model was proposed and implement a model that allows us to evaluate and enhance the quality of fundus images, which contributes to improving the efficiency and effectiveness of a subsequent diagnosis based on these images. On the one hand, for the evaluation of these images, a model based on a lightweight convolutional neural network architecture was developed, termed as Mobile Fundus Quality Network (MFQ-Net). This model has approximately 90% fewer parameters than those of the latest generation. For its evaluation, the Kaggle public data set was used with two sets of quality annotations, binary (good and bad) and three classes (good, usable and bad) obtaining an accuracy of 0.911 and 0.856 in the binary mode and three classes respectively in the classification of the fundus image quality. On the other hand, a method was developed for eye fundus quality enhancement termed as Pix2Pix Fundus Oculi Quality Enhancement (P2P-FOQE). This method is based on three stages which are; pre-enhancement: for color adjustment, enhancement: with a Pix2Pix network (which is a Conditional Generative Adversarial Network) as the core of the method and post-enhancement: which is a CLAHE adjustment for contrast and detail enhancement. This method was evaluated on a subset of quality annotations for the Kaggle public database which was re-classified for three categories (good, usable, and poor) by a specialist from the Fundación Oftalmolóica Nacional. With this method, the quality of these images for the good class was improved by 72.33%. Likewise, the image quality improved from the bad class to the usable class, and from the bad class to the good class by 56.21% and 29.49% respectively.La ingeniería busca diseñar, construir e implementar soluciones que permitan aumentar y/o mejorar la calidad de vida de los seres humanos. Igualmente, desde la medicina son generadas soluciones con los mismos fines, posibilitando que estas dos áreas del conocimiento convergan por un bien común. Con el trabajo de tesis “A Deep Learning Model to Assess and Enhance Eye Fundus Image Quality”, se propuso e implementó un modelo que permite evaluar y mejorar la calidad de las imágenes de fondo de ojo, lo cual contribuye a mejorar la eficiencia y eficacia de un posterior diagnóstico basado en estas imágenes. Para la evaluación de estás imágenes, se desarrolló un modelo basado en una arquitectura de red neuronal convolucional ligera, la cual fue llamada Mobile Fundus Quality Network (MFQ-Net). Este modelo posee aproximadamente 90% menos parámetros que aquellos de última generación. Para su evaluación se utilizó el conjunto de datos públicos de Kaggle con dos sets de anotaciones de calidad, binario (buena y mala) y tres clases (buena, usable y mala) obteniendo en la tareas de clasificación de la calidad de la imagen de fondo de ojo una exactitud de 0.911 y 0.856 en la modalidad binaria y tres clases respectivamente. Por otra parte, se desarrolló un método el cual realiza una mejora de la calidad de imágenes de fondo de ojo llamado Pix2Pix Fundus Oculi Quality Enhacement (P2P-FOQE). Este método está basado en tres etapas las cuales son; premejora: para ajuste de color, mejora: con una red Pix2Pix (la cual es una Conditional Generative Adversarial Network) como núcleo del método y postmejora: la cual es un ajuste CLAHE para contraste y realce de detalles. Este método fue evaluado en un subconjunto de anotaciones de calidad para la base de datos pública de Kaggle el cual fue re clasificado por un especialista de la Fundación Oftalmológica Nacional para tres categorías (buena, usable y mala). Con este método fue mejorada la calidad de estas imágenes para la clase buena en un 72,33%. Así mismo, la calidad de imagen mejoró de la clase mala a la clase utilizable, y de la clase mala a clase buena en 56.21% y 29.49% respectivamente.Línea de investigación: Visión por computadora para análisis de imágenes médicasMaestrí

Applications of generative adversarial networks in the diagnosis, prognosis, and treatment of ophthalmic diseases

Purpose: Generative adversarial networks (GANs) are key components of many artificial intelligence (AI) systems that are applied to image-informed bioengineering and medicine. GANs combat key limitations facing deep learning models: small, unbalanced datasets containing few images of severe disease. The predictive capacity of conditional GANs may also be extremely useful in managing disease on an individual basis. This narrative review focusses on the application of GANs in ophthalmology, in order to provide a critical account of the current state and ongoing challenges for healthcare professionals and allied scientists who are interested in this rapidly evolving field. Methods: We performed a search of studies that apply generative adversarial networks (GANs) in diagnosis, therapy and prognosis of eight eye diseases. These disparate tasks were selected to highlight developments in GAN techniques, differences and common features to aid practitioners and future adopters in the field of ophthalmology. Results: The studies we identified show that GANs have demonstrated capacity to: generate realistic and useful synthetic images, convert image modality, improve image quality, enhance extraction of relevant features, and provide prognostic predictions based on input images and other relevant data. Conclusion: The broad range of architectures considered describe how GAN technology is evolving to meet different challenges (including segmentation and multi-modal imaging) that are of particular relevance to ophthalmology. The wide availability of datasets now facilitates the entry of new researchers to the field. However mainstream adoption of GAN technology for clinical use remains contingent on larger public datasets for widespread validation and necessary regulatory oversight

RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark

Ophthalmologists have used fundus images to screen and diagnose eye diseases. However, different equipments and ophthalmologists pose large variations to the quality of fundus images. Low-quality (LQ) degraded fundus images easily lead to uncertainty in clinical screening and generally increase the risk of misdiagnosis. Thus, real fundus image restoration is worth studying. Unfortunately, real clinical benchmark has not been explored for this task so far. In this paper, we investigate the real clinical fundus image restoration problem. Firstly, We establish a clinical dataset, Real Fundus (RF), including 120 low- and high-quality (HQ) image pairs. Then we propose a novel Transformer-based Generative Adversarial Network (RFormer) to restore the real degradation of clinical fundus images. The key component in our network is the Window-based Self-Attention Block (WSAB) which captures non-local self-similarity and long-range dependencies. To produce more visually pleasant results, a Transformer-based discriminator is introduced. Extensive experiments on our clinical benchmark show that the proposed RFormer significantly outperforms the state-of-the-art (SOTA) methods. In addition, experiments of downstream tasks such as vessel segmentation and optic disc/cup detection demonstrate that our proposed RFormer benefits clinical fundus image analysis and applications. The dataset, code, and models are publicly available at https://github.com/dengzhuo-AI/Real-FundusComment: IEEE J-BHI 2022; The First Benchmark and First Transformer-based Method for Real Clinical Fundus Image Restoratio

AI image generation technology in ophthalmology: Use, misuse and future applications

Background: AI-powered image generation technology holds the potential to reshape medical practice, yet it remains an unfamiliar technology for both medical researchers and clinicians alike. Given the adoption of this technology relies on clinician understanding and acceptance, we sought to demystify its use in ophthalmology. To this end, we present a literature review on image generation technology in ophthalmology, examining both its theoretical applications and future role in clinical practice. // Methods: First, we consider the key model designs used for image synthesis, including generative adversarial networks, autoencoders, and diffusion models. We then perform a survey of the literature for image generation technology in ophthalmology prior to September 2024, presenting both the type of model used and its clinical application. Finally, we discuss the limitations of this technology, the risks of its misuse and the future directions of research in this field. // Results: Applications of this technology include improving AI diagnostic models, inter-modality image transformation, more accurate treatment and disease prognostication, image denoising, and individualised education. Key barriers to its adoption include bias in generative models, risks to patient data security, computational and logistical barriers to development, challenges with model explainability, inconsistent use of validation metrics between studies and misuse of synthetic images. Looking forward, researchers are placing a further emphasis on clinically grounded metrics, the development of image generation foundation models and the implementation of methods to ensure data provenance. // Conclusion: Compared to other medical applications of AI, image generation is still in its infancy. Yet, it holds the potential to revolutionise ophthalmology across research, education and clinical practice. This review aims to guide ophthalmic researchers wanting to leverage this technology, while also providing an insight for clinicians on how it may change ophthalmic practice in the future

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

UWAFA-GAN: Ultra-Wide-Angle Fluorescein Angiography Transformation via Multi-scale Generation and Registration Enhancement

Fundus photography, in combination with the ultra-wide-angle fundus (UWF) techniques, becomes an indispensable diagnostic tool in clinical settings by offering a more comprehensive view of the retina. Nonetheless, UWF fluorescein angiography (UWF-FA) necessitates the administration of a fluorescent dye via injection into the patient's hand or elbow unlike UWF scanning laser ophthalmoscopy (UWF-SLO). To mitigate potential adverse effects associated with injections, researchers have proposed the development of cross-modality medical image generation algorithms capable of converting UWF-SLO images into their UWF-FA counterparts. Current image generation techniques applied to fundus photography encounter difficulties in producing high-resolution retinal images, particularly in capturing minute vascular lesions. To address these issues, we introduce a novel conditional generative adversarial network (UWAFA-GAN) to synthesize UWF-FA from UWF-SLO. This approach employs multi-scale generators and an attention transmit module to efficiently extract both global structures and local lesions. Additionally, to counteract the image blurriness issue that arises from training with misaligned data, a registration module is integrated within this framework. Our method performs non-trivially on inception scores and details generation. Clinical user studies further indicate that the UWF-FA images generated by UWAFA-GAN are clinically comparable to authentic images in terms of diagnostic reliability. Empirical evaluations on our proprietary UWF image datasets elucidate that UWAFA-GAN outperforms extant methodologies. The code is accessible at https://github.com/Tinysqua/UWAFA-GAN