iMedImage Technical Report
Journal:
arXiv
Published Date:
Mar 27, 2025
Abstract
Background: Chromosome karyotype analysis is crucial for diagnosing
hereditary diseases, yet detecting structural abnormalities remains
challenging. While AI has shown promise in medical imaging, its effectiveness
varies across modalities. Leveraging advances in Foundation Models that
integrate multimodal medical imaging for robust feature extraction and accurate
diagnosis, we developed iMedImage, an end-to-end model for general medical
image recognition, demonstrating strong performance across multiple imaging
tasks, including chromosome abnormality detection. Materials and Methods: We
constructed a comprehensive medical image dataset encompassing multiple
modalities from common medical domains, including chromosome, cell, pathology,
ultrasound, X-ray, CT, and MRI images. Based on this dataset, we developed the
iMedImage model, which incorporates the following key features: (1) a unified
representation method for diverse modality inputs and medical imaging tasks;
(2) multi-level (case-level, image-level, patch-level) image recognition
capabilities enhanced by Chain of Thought (CoT) embedding and Mixture of
Experts (MoE) strategies. Results: The test set comprised data from 12
institutions across six regions in China, covering three mainstream scanning
devices, and included naturally distributed, unscreened abnormal cases. On this
diverse dataset, the model achieved a fully automated chromosome analysis
workflow, including segmentation, karyotyping, and abnormality detection,
reaching a sensitivity of 92.75% and a specificity of 91.5%. Conclusion: We
propose iMedImage, an end-to-end foundation model for medical image analysis,
demonstrating its superior performance across various medical imaging tasks.
iMedImage provides clinicians with a precise imaging analysis tool and
contributes to improving diagnostic accuracy and disease screening.
