ReviewInternational journal of multimedia information retrieval2022
Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey.
Review in International journal of multimedia information retrieval, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of generative adversarial networks (GANs) in cell microscopy: Trends, practices, and impact on image augmentation.PloS one · 2025Pooled it
- A hybrid ConvNeXt-ViT framework with differential evolution optimization for breast cancer classification.Scientific reports · 2026Article
- Segmentation and classification of hippocampal subregions using multi-task generative adversarial networks.Scientific reports · 2026Article
- A Method for Industrial Smoke Video Semantic Segmentation Using DeffNet with Inter-Frame Adaptive Variable Step Size Based on Fuzzy Control.Sensors (Basel, Switzerland) · 2026Article
- A deep learning framework for breast cancer diagnosis using Swin Transformer and Dual-Attention Multi-scale Fusion Network.Scientific reports · 2026Article
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Deep learning for multi-modal medical image segmentation: a survey and comparative study.Brain imaging and behavior · 2025Article
- Article
- Deep learning for malignant lymph node segmentation and detection: a review.Frontiers in immunology · 2025Review
- Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis.Breast cancer research : BCR · 2024Review
- Uncover This Tech Term: Generative Adversarial Networks.Korean journal of radiology · 2024Article
- Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey.Life (Basel, Switzerland) · 2023Review
- An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19.Journal of signal processing systems · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.