Vol. 23 No. Special Issue 3 (2024): Mapana - Journal of Sciences
Research Articles

SSCMIRG: Self-Supervised Contrastive Learning for Medical Images with Report Generation

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  • Rahul Kumar,
  • Arulkumar N
Rahul Kumar
CHRIST (Deemed to be University), Bengaluru, Karnataka, India
Arulkumar N
CHRIST (Deemed to be University), Bengaluru, Karnataka, India
DOI: https://doi.org/10.12723/mjs.sp3.6

Published 2024-11-06

Keywords

  • Self-Supervised Learning,
  • Contrastive Learning,
  • Medical Report Generation,
  • Medical Image Classification

Copyright (c) 2024

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

Machine learning has shown promising applications in interpreting medical images, offering accurate results in areas lacking radiologists. While not replacing radiologists, image captioning assists in diagnosis and therapy. Self-supervised contrastive learning is vital for medical image analysis, inspired by human categorization learning. This technique classifies unlabeled data to generate reports, aiding doctors in making better diagnoses and treatment plans. By predicting and grouping image components, medical report formation ensures accurate and consistent record-keeping. Contrastive learning enables models to learn from unlabeled data by identifying similarities and differences. These advancements have become crucial in machine learning, enhancing medical imaging and opening doors for further improvements and research.

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