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Related Experiment Video

Updated: Jul 25, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI.

Duygu Sinanc Terzi¹, Nuh Azginoglu²

¹Department of Computer Engineering, Amasya University, Amasya 05100, Turkey.

Diagnostics (Basel, Switzerland)

|June 28, 2023

Summary

This summary is machine-generated.

Transfer learning using medical image datasets significantly outperforms natural image datasets for medical object detection, showing better success and convergence. In-domain transfer learning proves more efficient than cross-domain approaches, even with less data.

Keywords:

brain MRI brain tumor detection object detection segmentation transfer learning

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Area of Science:

Medical Imaging
Computer Vision
Deep Learning

Background:

Transfer learning is crucial for data-scarce domains like medical imaging.
The efficacy of natural image datasets for pre-training in medical fields remains debated.
Quantitative comparison of transfer learning strategies is needed for medical object detection.

Purpose of the Study:

To quantitatively compare transfer learning for medical object detection using natural vs. medical image datasets.
To evaluate different weight initialization methods for transfer learning.
To assess the impact of data augmentation on transfer learning performance.

Main Methods:

Utilized Mask R-CNN architecture for object detection and segmentation.
Employed MS COCO (natural) and BraTS 2020 (medical) datasets for pre-training.
Gazi Brains 2020 dataset served as the target for medical object detection.
Compared five different weight initialization strategies.

Main Results:

Medical image pre-training yielded 10% higher success and 24% better convergence than natural image pre-training.
Transfer learning with MS COCO or random weights showed performance similar to data augmentation.
In-domain transfer learning demonstrated higher efficiency than cross-domain transfer learning, irrespective of data volume.

Conclusions:

In-domain transfer learning, particularly with medical datasets, is superior for medical object detection tasks.
The Gazi Brains 2020 dataset is introduced for in-domain transfer learning in brain MRI analysis.
Deep neural networks benefit from knowledge transfer within the same domain for improved medical image analysis.