ICIDDM2K25 PPT Format 11-12 April 2025 (1).pptx

International Conference on Interactive Design and Digital Manufacturing
(ICIDDM 2K25)
ORGANIZED BY
SRM Institute of Science and
Technology Tiruchirappalli
DATE: APRIL 11-12, 2025

11 & 12 April
2025
1
“Generative Adversarial Networks (GANs) in Healthcare: A Systematic
Literature Review”
International Conference on Interactive Design and Digital Manufacturing (ICIDDM 2K25)
Niti Patil1
, Dr. Shweta Agrawal2
.
1
Dept. of CSE, Indore Institute of Science and Technology Indore (M.P.),
2
Dept. of CSE, Indore Institute of Science and Technology Indore (M.P.),
Presented by- Niti Patil

INTRODUCTION
11 & 12 April 2025
(ICIDDM 2K25) 2
 Generative Adversarial Networks (GANs) are a revolutionary paradigm in machine learning
and artificial intelligence (AI) that enables the creation of synthetic data that closely relates
to real-world data.
 These networks consist of two neural networks: the discriminator, which assesses the
accuracy of input, and the generator, which generates artificial data.
 They have found innovative applications in diagnostic algorithms, medical imaging
systems, and datasets, virtual simulations for medical training, enhancing practical skills.
 This review highlights the revolutionary potential of GANs in clinical research and
healthcare while highlighting the necessity of responsible and careful practices in their
deployment. The paper provides a detailed analysis of GAN architectures relevant to
healthcare.

LAYOUT OF WORK
11 & 12 April 2025
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GAN OPERATIONAL MECHANISM
11 & 12 April 2025
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VARIOUS GAN ARCHITECTURES USED IN HEALTHCARE
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• Medical Image Generation (Breast Cancer Detection)[22]
DCGAN
• Synthetic Brain MRI Generation (Tumor Classification)[3]
CGAN
• Image Translation (Low to High-Quality CT Scans)[4]
CycleGAN
• Super-Resolution MRI Imaging (Enhanced Brain Scans) [5]
SRGAN
• High-Resolution Brain MRI Generation (Cancer Detection)[6]
PGGAN
• Paired Image Translation (MRI to CT Scan)[7]
Pix2Pix GAN
• Lesion Detection (Skin Cancer Diagnosis)[24]
Attention-GAN
• Synthetic 3D Medical Images (Tumor Modeling)[9]
3D GAN
• Decentralized Model Training (Patient Data Privacy)[10]
Federated GAN
• Privacy-Preserving Synthetic Patient Data (Secure Research)[11]
PPGAN

APPLICATIONS OF GANS IN HEALTHCARE
11 & 12 April 2025
(ICIDDM 2K25) 2
Medical
Imaging
Artificial data
augmentation
and cross-
modality image
translation[8][12]
Drug
Discovery
Identifying drug
candidates and
simulating
pharmacodynami
cs [13][14]
Personaliz
ed
Treatment
Patient-based
simulations for
treatment
planning[15][16]
Genomic
Research
Synthetic
genomic data for
studying rare
diseases[17][18]
Augmente
d & Virtual
Reality
Surgical
simulations for
training[9][19]
Privacy-
Preserving
GANs
Synthetic data
for research
without privacy
breaches[21]

ETHICAL CONSIDERATIONS AND CHALLENGES
11 & 12 April 2025
(ICIDDM 2K25) 2
 Privacy of Data: Safeguarding patient privacy is crucial when using GANs for data
generation.
 Regulation and Accountability: As GANs become more prevalent in healthcare, clear
frameworks are needed to define accountability for AI-generated actions and ensure safety
and efficacy.
 Bias in Data Generation: GANs may reinforce biases in training datasets, leading to unfair
healthcare outcomes.
 Transparent AI Decision-Making: Enhancing transparency in GAN-generated data and
forecasts is essential for building trust among healthcare professionals and patients. Clear
guidelines should define how GANs operate and produce outcomes.
[21][46][47[48]

COMPARATIVE STUDY
11 & 12 April 2025
(ICIDDM 2K25) 2
Task GAN Model Used Performance Challenges Dataset & Size
Evaluation
Metrics
Reference
Synthetic
Mammogram
Generation
Deep
Convolutional
GAN (DCGAN)
By using DCGAN, we can generate synthetic
mammograms, which help in creating larger
and more diverse training datasets for breast
cancer detection models. This ultimately
improves their accuracy and reliability.
The challenge here is ensuring that the
generated images look realistic while
avoiding mode collapse, where the model
starts producing repetitive or low-diversity
samples.
INbreast Dataset
(410 images)
FID, IS, SSIM
[2],[35],[36],
[37], [49]
Synthetic Genomic
Data
Privacy-Preserving
GAN (PPGAN)
With PPGAN, we can create synthetic genomic
datasets that enable research on rare genetic
disorders while protecting patient privacy. This
helps in advancing personalized medicine.
The main challenge is making sure that
the synthetic data retains important
genetic patterns without accidentally
revealing sensitive correlations from real
patient data.
Adult dataset
(45222 individuals
data)
pMSE, MMD
[16],[38],[39],
[40],[42],[50]
Synthetic 3D Brain
MRIs, Brain MRI
Image
Synthesis,High-
Resolution Brain
MRIs
3D GAN,
Progressive
Growing GAN
(PGGAN),
Using these models, we can generate high-
resolution 3D MRI scans, which are crucial for
training AI models in neuroimaging and tumor
modeling.
With CGANs, we can generate brain MRI
images that are conditioned on specific tumor
types, allowing researchers to create diverse
datasets tailored to different diagnostic needs.
With PGGAN, we can generate ultra-high-
resolution brain MRI scans, which significantly
improve the performance of AI-driven cancer
detection models.
Since 3D medical images are high-
dimensional, training these models
requires significant computational power,
and ensuring stable training is a major
challenge.
The challenge is fine-tuning the GAN to
generate images that accurately reflect
different tumor characteristics while
avoiding overfitting to a specific type.
The challenge exists in the cost of high
computational progressive training and
the risk of overfitting to particular
patterns.
Brain Tumor MRI
Dataset ( 7023
images of human
brain MRI images)
SSIM, PSNR, FID
[3],[6],[7],[8],
[9],[11], [12],
[34],[29],[26]
[51]

COMPARATIVE STUDY
11 & 12 April 2025
(ICIDDM 2K25) 2
CT Image
Enhancement, High-
Resolution Medical
Images,Cross-
Modality Image
Translation
CycleGAN,
Attention-
GAN,Super-
Resolution GAN
(SRGAN),
PGGAN,CycleGAN,
Pix2Pix GAN
By applying these models, we can enhance low-
dose CT scans, improving their quality while
reducing radiation exposure for patients.
Using these models, we can upscale medical
images, making them clearer and more useful for
diagnosis.
By using these models, we can transform images,
such as converting MRI scans into CT scans,
reducing the need for multiple imaging
procedures.
The key challenge is preserving fine
anatomical details during the enhancement
process while avoiding unwanted artifacts.
The challenge is maintaining spatial
consistency and ensuring that the enhanced
images still accurately represent medical
structures.
The main challenge is handling noise and
ensuring that the anatomical structures
remain consistent between different
modalities.
NIH Chest X-ray
(112,120 images)
MAE, RMSE, PSNR,
SSIM
[4],[5],[6],[8],
[9],[27],[6],[28],
[30],[42],[52]
Lesion Detection Attention-GAN
With Attention-GAN, we can improve lesion
detection by allowing the model to focus on
important regions in medical images, making
diagnoses more accurate.
The difficulty is training the GAN to correctly
focus on relevant areas without introducing
bias or ignoring subtle lesions.
ISLES 2015 (28
cases), BraTS 2020
(369 cases)
Dice Score, IoU, F1-
score
[10],[31],[53]
Privacy-Preserving
Dataset Creation
Privacy-Preserving
GAN (PPGAN)
By leveraging PPGAN, we can generate synthetic
patient datasets that closely resemble real data,
enabling research collaboration while ensuring
patient privacy.
The biggest challenge is striking the right
balance between privacy preservation and
maintaining the statistical accuracy of the
generated data.
Adult dataset (45222
individuals data)
pMSE, MMD [13],[33],[50]

COMPARATIVE STUDY
11 & 12 April 2025
(ICIDDM 2K25) 2
Collaborative
Training Without
Data Sharing
Federated GAN
With Federated GAN, we can train models
across multiple institutions without sharing
sensitive patient data, allowing for large-scale
collaborations.
The main challenge is managing
communication overhead and ensuring
model convergence across decentralized
environments.
PhysioNet
Challenge 2012
(4,000 patients),
MIMIC-III
Accuracy, AUC-
ROC
[14], [15],[32]
[54]
Drug Discovery
Conditional GAN
(CGAN)
Using CGANs, we can generate new molecular
structures, speeding up drug discovery by
identifying potential therapeutic compounds.
The challenge is ensuring that the
generated molecules are chemically valid
and not just trivial solutions.
ZINC dataset
(250,000
molecules)
Validity, Novelty,
Diversity, LogP
[3],[13],[17],
[55]
Virtual Surgical
Training
Pix2Pix GAN
By applying Pix2Pix, we can create realistic
surgical simulations, helping surgeons practice
in a virtual environment and improving patient
outcomes.
The challenge is balancing realism with
computational efficiency, ensuring smooth
and interactive real-time simulations.
Surgical Video
Dataset (200
videos)
Realism Score,
Latency
[18],[41],[42],
[56]
Personalized
Treatment Plans
CycleGAN,
Conditional
GAN,Federated
GAN
(FedGAN),Progres
sively Growing
GAN
(PGGAN),Privacy-
Preserving GAN
(PPGAN)
By using GANs we can create personalized
treatment plans by generating synthetic patient
data, simulating disease progression, and
optimizing treatment strategies.
GAN-based personalized treatment
planning faces challenges like data bias,
which can lead to unreliable
recommendations, and model
interpretability, making clinical trust
difficult. Additionally, regulatory concerns
and high computational costs hinder real-
world deployment.
SEER dataset
(500,000+
records)
AUC-ROC,
Accuracy,
Precision, Recall
[1],[3],[6],[43],
[44],[45],[57]

COMPARATIVE STUDY
11 & 12 April 2025
(ICIDDM 2K25) 2

CONCLUSION
With the potential to significantly transform the healthcare landscape, Generative Adversarial Networks
represent a revolutionary advancement in machine learning. GANs can enhance medical imaging,
facilitate personalized treatment planning, accelerate drug discovery, and improve training
methodologies for healthcare professionals by the generating good-quality synthetic data. Although, as
we research and analyse the inclusion of GANs into healthcare, careful consideration of implications for
ethics, accountability, and data safeguarding is crucial. Additionally, future research work should focus
on improving GAN designs to handle their convergence with new technologies [25], particularly
medical care applications, and creating robust frameworks for the ethical utilization of AI in healthcare.
11 & 12 April 2025 International Conference on Interactive Design and Digital Manufacturing (ICIDDM 2K25) 13

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