Enhancing Medical Image Analysis: A Pipeline Combining Synthetic Image Generation and Super-Resolution

Cancer is a leading cause of mortality worldwide, with breast and lung cancer being the most prevalent globally. Early and accurate diagnosis is crucial for successful treatment, and medical imaging techniques play a pivotal role in achieving this. This paper proposes a novel pipeline that leverages generative artificial intelligence to enhance medical images by combining synthetic image generation and super-resolution techniques. The framework is validated in two medical use cases (breast and lung cancers), demonstrating its potential to improve the quality and quantity of medical imaging data, ultimately contributing to more precise and effective cancer diagnosis and treatment. Overall, although some limitations do exist, this paper achieved satisfactory results for an image size which is conductive to specialist analysis, and further expands upon this field’s capabilities.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-23
DOI
https://doi.org/10.5281/zenodo.15426165
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
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article

Enhancing Medical Image Analysis: A Pipeline Combining Synthetic Image Generation and Super-Resolution

Zenodo (CERN European Organization for Nuclear Research)
AI in cancer detection
article

Enhancing Medical Image Analysis: A Pipeline Combining Synthetic Image Generation and Super-Resolution

article en

Abstract

Cancer is a leading cause of mortality worldwide, with breast and lung cancer being the most prevalent globally. Early and accurate diagnosis is crucial for successful treatment, and medical imaging techniques play a pivotal role in achieving this. This paper proposes a novel pipeline that leverages generative artificial intelligence to enhance medical images by combining synthetic image generation and super-resolution techniques. The framework is validated in two medical use cases (breast and lung cancers), demonstrating its potential to improve the quality and quantity of medical imaging data, ultimately contributing to more precise and effective cancer diagnosis and treatment. Overall, although some limitations do exist, this paper achieved satisfactory results for an image size which is conductive to specialist analysis, and further expands upon this field’s capabilities.

Zenodo (CERN European Organization for Nuclear Research)
Universidade do Porto (PT), INESC TEC (PT), University of Coimbra (PT)
Good health and well-being
Openalex Percentile: Top 75%
AI in cancer detection
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Enhancing Medical Image Analysis: A Pipeline Combining Synthetic Image Generation and Super-Resolution · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS