Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.

Medical image analysis is becoming a critical deep learning application based on machine learning by contributing to developing a more sustainable health system, which probably overpowers doctors’ workload drastically. With the advancement in deep learning technique, there is rise in the samples for training of diagnosis and treatment models. Generative Adversarial Networks (GANs) have sought attention in the field of medical image processing by their outstanding image generation capabilities and data generation without mapping the probability density function explicitly. GAN methods simulate the actual data distribution and reconstruct estimated accurate data. Medical images are available in less amounts, and the acquiring of medical image annotations is costly; therefore, generated data can solve the problem of data insufficiency or data imbalance. GANs are proven very useful in data augmentation and image translation. These qualities of GAN have fascinated researchers, and rapid adoption is noticed in the reconstruction, synthesis, segmentation, denoising detection, and classification of medical images. Finally, GAN models are extensively used for feature selection and extraction for medical image analysis and early diagnosis of diseases.

The emergence of generative adversarial networks (GANs) has provided new technology and framework for the application of medical images. Specifically, a GAN requires little to no labeled data to obtain high-quality data that can be generated through competition between the generator and discriminator networks. Therefore, GANs are rapidly proving to be a state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the principles of GANs and their various variants, deep convolutional GAN, conditional GAN, Wasserstein GAN, Info-GAN, boundary equilibrium GAN, and cycle-GAN. All various GANs have found success in medical imaging tasks, including medical image enhancement, segmentation, classification, reconstruction, and synthesis. Furthermore, we summarize the data processing methods and evaluation indicators. Finally, we note the limitations of existing methods and the existing challenges that need to be addressed in this field. Although GANs are in the initial stage of development in medical image processing, it will have a great prospect in the future.

Transformer-based deep learning architectures have achieved notable success across various medical image analysis tasks, driven by the global modeling capabilities of the self-attention mechanism. However, Transformer-based methods exhibit significant computational complexity and a large number of parameters, rendering them challenging to apply effectively in practical medical scenarios. Compared with Transformers, large-kernel Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs) offer more efficient inference while retaining global contextual awareness. Therefore, we rethink the role of large-kernel CNNs and MLPs in medical image analysis and leverage them to replace the heavy self-attention operation, to strike a better balance between performance and efficiency. Specifically, we propose backbone models for medical image classification and segmentation, featured by three lightweight modules: Linear Attention Feed Forward Network (FFN) for enhancing lesion features, Spatial Encoding Module for integrating multi-scale lesion information, and Smooth Depth-Wise Convolution (DwConv) FFN for efficient interaction of channel features. Composed solely of lightweight convolutional and MLP operations, our method achieves a better balance between performance and efficiency, validated by the superior performances on five datasets with varying data scales and diseases, with 98.39% on SARS-COV2-CT-Scan, 98.12% on Monkeypox Skin Lesion Dataset, 98.58% on Large COVID-19-CT scan slice, 79.45% on Synapse and 91.28% on ACDC. The low computational cost, high-performance with limited training data, and generalizability to various of medical tasks make the proposed method a promising and practical solution for medical image classification and segmentation.

ResGANet: Residual group attention network for medical image classification and segmentation.

Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends

A review of medical ocular image segmentation

Generative adversarial networks in medical image segmentation: A review

Generative adversarial networks (GANs) have shown its prominent performance to the world and be exploited in many areas. For example, generating human face image, translating image between different domain, generating supplementary data for tasks which lacks training and labeled data. Specially, GANs also shows its potential in medical image analysis. It can tackle the problems in medical image analysis such as medical image translation, segmentation, reconstruction, detection and image classification. Being capable of generating images at amazing level of realisim, GANs gives the opportunity to resolve the problem that labeled data for those rare diseases cannot meet the need for medical field. In this paper, we give an overview of GANs and its application in medical image analysis. Defects and advantages of those GANs methods are also thoroughly reviewed. We also discuss its potential improvement in future. We review those most frequently methods published until now. And essential details about papers we discussed in this paper and access to that paper is attached at the endpage.

At present, segmentation for medical image is mainly based on fully supervised model training, which consumes a lot of time and labor for dataset labeling. To address this issue, we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries. The network is mainly composed of two parts: a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results. In the initial stage of network training, a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination capabilities. Then a semi-supervised method is adopted to train the model, in which the discriminant network will generate pseudo-labels on the results of the segmentation for semi-supervised training of the segmentation network. The proposed method can use a small part of annotated dataset to realize the segmentation of medical images and effectively solve the problem of insufficient medical image annotation data.

Apply revolutionary deep learning technology to the fast-growing field of medical image segmentation Precise medical image segmentation is rapidly becoming one of the most important tools in medical research, diagnosis, and treatment. The potential for deep learning, a technology which is already revolutionizing practice across hundreds of subfields, is immense. The prospect of using deep learning to address the traditional shortcomings of image segmentation demands close inspection and wide proliferation of relevant knowledge. Deep Learning Applications in Medical Image Segmentation meets this demand with a comprehensive introduction and its growing applications. Covering foundational concepts and its advanced techniques, it offers a one-stop resource for researchers and other readers looking for a detailed understanding of the topic. It is deeply engaged with the main challenges and recent advances in the field of deep-learning-based medical image segmentation. Readers will also find: Analysis of deep learning models, including FCN, UNet, SegNet, Dee Lab, and many moreDetailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systemsRecent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structuresAnalyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosisExplores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentationIdentifies and discusses the key challenges faced in medical image segmentation using deep learning techniquesProvides an overview of the latest advancements, applications, and future trends in deep learning for medical image analysis Deep Learning Applications in Medical Image Segmentation is ideal for academics and researchers working with medical image segmentation, as well as professionals in medical imaging, data science, and biomedical engineering.

Medical images play a crucial role in modern healthcare diagnostics and treatment. However, many medical images suffer from limitations in resolution, potentially impeding a comprehensive understanding of a patient's condition by healthcare professionals. This comprehensive review delves into the applications of Generative Adversarial Networks (GANs) in medical image super-resolution reconstruction to address this challenge. In the Methods section, this paper first focused on the direction of medical image classification, including cell classification of histopathological images and synthetic data enhancement using GANs to improve liver lesion classification. Subsequently, this paper focused on the direction of medical image segmentation, looking into the use of Structure-Corrected Adversarial Networks (SCAN) for organ segmentation in chest radiographs and Deep Adversarial Networks for biomedical image segmentation using unannotated images. In the Applications and Discussion section, this paper thoroughly examined the current progress of GANs in telemedicine diagnosis and disease state generation and prediction. This paper emphasized the significant potential of GAN technology in telemedicine while outlining the current constraints and challenges. Furthermore, this paper highlighted the prospects of GANs in medical image super-resolution reconstruction and how they affect the discipline of medical imaging. This comprehensive review consolidates the latest research findings on GANs in medical image super-resolution reconstruction, underscoring their importance in the realm of healthcare. By critically analysing existing literature, this paper provides valuable insights for medical image analysts are researchers while inspiring future research directions and innovations.

Rectal cancer (RC) is a globally prevalent malignant tumor, presenting significant challenges in its management and treatment. Currently, magnetic resonance imaging (MRI) offers superior soft tissue contrast and radiation-free effects for RC patients, making it the most widely used and effective detection method. In early screening, radiologists rely on patients' medical radiology characteristics and their extensive clinical experience for diagnosis. However, diagnostic accuracy may be hindered by factors such as limited expertise, visual fatigue, and image clarity issues, resulting in misdiagnosis or missed diagnosis. Moreover, the distribution of surrounding organs in RC is extensive with some organs having similar shapes to the tumor but unclear boundaries; these complexities greatly impede doctors' ability to diagnose RC accurately. With recent advancements in artificial intelligence, machine learning techniques like deep learning (DL) have demonstrated immense potential and broad prospects in medical image analysis. The emergence of this approach has significantly enhanced research capabilities in medical image classification, detection, and segmentation fields with particular emphasis on medical image segmentation. This review aims to discuss the developmental process of DL segmentation algorithms along with their application progress in lesion segmentation from MRI images of RC to provide theoretical guidance and support for further advancements in this field.

Recently emerged SAM-Med2D represents a state-of-the-art advancement in medical image segmentation. Through fine-tuning the Large Visual Model, Segment Anything Model (SAM), on extensive medical datasets, it has achieved impressive results in cross-modal medical image segmentation. However, its reliance on interactive prompts may restrict its applicability under specific conditions. To address this limitation, we introduce SAM-AutoMed, which achieves automatic segmentation of medical images by replacing the original prompt encoder with an improved MobileNet v3 backbone. The performance on multiple datasets surpasses both SAM and SAM-Med2D. Current enhancements on the Large Visual Model SAM lack applications in the field of medical image classification. Therefore, we introduce SAM-MedCls, which combines the encoder of SAM-Med2D with our designed attention modules to construct an end-to-end medical image classification model. It performs well on datasets of various modalities, even achieving state-of-the-art results, indicating its potential to become a universal model for medical image classification.

Deep learning (DL) has transformed medical image analysis over the past decade, enabling automated, accurate, and scalable solutions for detection, classification, segmentation, and synthesis of medical images. This review synthesizes the evolution of major deep learning architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformers and focuses on their specific applications in medical imaging modalities such as X-ray, CT, MRI, ultrasound, and histopathology. We discuss training strategies, data challenges, evaluation metrics, and clinical translation barriers. Finally, we present comparative tables, figure placeholders for common architectures, and an outlook on emerging directions including self-supervised learning, federated learning, and foundation models in medical imaging. The review includes key works from 2012–2025 to provide both foundational and contemporary context.

The rapid advancements in medical imaging technologies have significantly enhanced diagnostic accuracy and clinical decision-making in modern healthcare. Image segmentation and deep learning have emerged as transformative tools among these advancements. This article explores the pivotal role of image segmentation and deep learning in medical imaging, detailing their methodologies, applications, challenges, and future directions. Deep learning, particularly Convolutional Neural Networks (CNNs), has revolutionized medical imaging by automating the analysis of complex datasets and improving diagnostic precision. Image segmentation, a fundamental component of medical imaging, allows for delineating specific structures such as organs, tissues, and pathological regions. Together, these technologies have been applied in diverse fields, including oncology, cardiology, neurology, and ophthalmology, enabling applications such as tumor detection, organ segmentation, disease progression monitoring, and treatment planning. However, despite its transformative potential, the integration of deep learning into medical imaging faces several challenges. These include data scarcity, privacy concerns, interpretability issues, and regulatory hurdles. The article discusses various strategies to address these challenges, such as data augmentation, transfer learning, and the development of explainable AI models to ensure transparency and trustworthiness. Evaluation metrics, such as accuracy, sensitivity, specificity, and Dice Similarity Coefficient (DSC), are essential for assessing model performance. Rigorous clinical validation and regulatory approval are crucial to integrating deep learning systems into clinical workflows effectively. Looking ahead, the future of deep learning in medical imaging holds immense promise. Innovations like multimodal imaging, personalized medicine, and AI-driven automation are set to further revolutionize the field, enhancing the efficiency and accuracy of diagnostics. Collaborative efforts between clinicians, researchers, and AI developers will play a vital role in overcoming current limitations and driving progress. This article concludes by emphasizing the transformative potential of deep learning and image segmentation in medical imaging, highlighting their ability to improve diagnostic accuracy, streamline clinical workflows, and ultimately, enhance patient care. By addressing current challenges and continuing to innovate, these technologies are poised to redefine the landscape of medical diagnostics and treatment in the years to come.