# Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation

arXiv: https://arxiv.org/abs/2410.13099 · HTML: https://mlsanity.com/paper/2410.13099/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function](https://mlsanity.com/paper/2408.11839/) (2024)
- [Attention Mechanism and Context Modeling System for Text Mining Machine Translation](https://mlsanity.com/paper/2408.04216/) (2024)
- [Breast Cancer Image Classification Method Based on Deep Transfer Learning](https://mlsanity.com/paper/2404.09226/) (2024)
- [A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images](https://mlsanity.com/paper/2409.15332/) (2024)
- [Contrastive Learning for Knowledge-Based Question Generation in Large Language Models](https://mlsanity.com/paper/2409.13994/) (2024)
- [Convolutional Neural Networks for Predictive Modeling of Lung Disease](https://mlsanity.com/paper/2408.12605/) (2024)
- [Reducing Bias in Deep Learning Optimization: The RSGDM Approach](https://mlsanity.com/paper/2409.15314/) (2024)
- [Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation](https://mlsanity.com/paper/2409.13868/) (2024)
- [Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature](https://mlsanity.com/paper/2409.14000/) (2024)
- [Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection](https://mlsanity.com/paper/2409.12347/) (2024)
- [Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation](https://mlsanity.com/paper/2407.21328/) (2024)
- [Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI](https://mlsanity.com/paper/2311.16213/) (2023)

## Built on by (most cited first, 10 of 10)

- [Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation](https://mlsanity.com/paper/2411.03572/) (2024)
- [Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning](https://mlsanity.com/paper/2411.06326/) (2024)
- [Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues](https://mlsanity.com/paper/2411.06374/) (2024)
- [Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification](https://mlsanity.com/paper/2411.12151/) (2024)
- [An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction](https://mlsanity.com/paper/2412.02211/) (2024)
- [Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study](https://mlsanity.com/paper/2410.20792/) (2024)
- [Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis](https://mlsanity.com/paper/2410.24046/) (2024)
- [Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments](https://mlsanity.com/paper/2411.05346/) (2024)
- [A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation](https://mlsanity.com/paper/2411.12157/) (2024)
- [Bridging spatial awareness and global context in medical image segmentation](https://mlsanity.com/paper/2512.06560/) (2025)

## Abstract (arXiv)

Recent advancements in artificial intelligence (AI) have precipitated a paradigm shift in medical imaging, particularly revolutionizing the domain of brain imaging. This paper systematically investigates the integration of deep learning -- a principal branch of AI -- into the semantic segmentation of brain images. Semantic segmentation serves as an indispensable technique for the delineation of discrete anatomical structures and the identification of pathological markers, essential for the diagnosis of complex neurological disorders. Historically, the reliance on manual interpretation by radiologists, while noteworthy for its accuracy, is plagued by inherent subjectivity and inter-observer variability. This limitation becomes more pronounced with the exponential increase in imaging data, which traditional methods struggle to process efficiently and effectively. In response to these challenges, this study introduces the application of adversarial neural networks, a novel AI approach that not only automates but also refines the semantic segmentation process. By leveraging these advanced neural networks, our approach enhances the precision of diagnostic outputs, reducing human error and increasing the throughput of imaging data analysis. The paper provides a detailed discussion on how adversarial neural networks facilitate a more robust, objective, and scalable solution, thereby significantly improving diagnostic accuracies in neurological evaluations. This exploration highlights the transformative impact of AI on medical imaging, setting a new benchmark for future research and clinical practice in neurology.