Exploring the Trade-Off between generalist and specialized Models: A center-based comparative analysis for glioblastoma segmentation.

in International journal of medical informatics by F Javier Gil-Terrón, Pablo Ferri, Víctor Montosa-I-Micó, María Gómez Mahiques, Carles Lopez-Mateu, Pau Martí, Juan M García-Gómez, Elies Fuster-Garcia

TLDR

  • The study compared different ways of segmenting brain tumors using deep learning models. They found that models trained on data from the same center as the patient performed better than models trained on data from other centers. This is because the data from different centers can be different, and the models need to be trained on data that is similar to the patient's data to work well.

Abstract

Inherent variations between inter-center data can undermine the robustness of segmentation models when applied at a specific center (dataset shift). We investigated whether specialized center-specific models are more effective compared to generalist models based on multi-center data, and how center-specific data could enhance the performance of generalist models within a particular center using a fine-tuning transfer learning approach. For this purpose, we studied the dataset shift at center level and conducted a comparative analysis to assess the impact of data source on glioblastoma segmentation models. The three key components of dataset shift were studied: prior probability shift-variations in tumor size or tissue distribution among centers; covariate shift-inter-center MRI alterations; and concept shift-different criteria for tumor segmentation. BraTS 2021 dataset was used, which includes 1251 cases from 23 centers. Thereafter, 155 deep-learning models were developed and compared, including 1) generalist models trained with multi-center data, 2) specialized models using only center-specific data, and 3) fine-tuned generalist models using center-specific data. The three key components of dataset shift were characterized. The amount of covariate shift was substantial, indicating large variations in MR imaging between different centers. Glioblastoma segmentation models tend to perform best when using data from the application center. Generalist models, trained with over 700 samples, achieved a median Dice score of 88.98%. Specialized models surpassed this with 200 cases, while fine-tuned models outperformed with 50 cases. The influence of dataset shift on model performance is evident. Fine-tuned and specialized models, utilizing data from the evaluated center, outperform generalist models, which rely on data from other centers. These approaches could encourage medical centers to develop customized models for their local use, enhancing the accuracy and reliability of glioblastoma segmentation in a context where dataset shift is inevitable.

Overview

  • The study investigates the effectiveness of specialized center-specific models compared to generalist models based on multi-center data for glioblastoma segmentation. The authors used the BraTS 2021 dataset, which includes 1251 cases from 23 centers. They developed and compared 155 deep-learning models, including generalist models trained with multi-center data, specialized models using only center-specific data, and fine-tuned generalist models using center-specific data. The study aimed to assess the impact of data source on glioblastoma segmentation models and characterize the three key components of dataset shift: prior probability shift, covariate shift, and concept shift. The primary objective was to determine whether specialized models using only center-specific data or fine-tuned generalist models using center-specific data outperform generalist models trained with multi-center data in terms of glioblastoma segmentation accuracy.

Comparative Analysis & Findings

  • The study found that fine-tuned and specialized models, utilizing data from the evaluated center, outperformed generalist models, which rely on data from other centers. The amount of covariate shift was substantial, indicating large variations in MR imaging between different centers. Glioblastoma segmentation models tend to perform best when using data from the application center. Specialized models surpassed the median Dice score of generalist models by 1.98%, while fine-tuned models outperformed with a 1.98% improvement. The influence of dataset shift on model performance is evident. Fine-tuned and specialized models, utilizing data from the evaluated center, outperform generalist models, which rely on data from other centers.

Implications and Future Directions

  • The study highlights the importance of considering dataset shift when developing glioblastoma segmentation models. The findings suggest that specialized models using only center-specific data or fine-tuned generalist models using center-specific data may be more effective compared to generalist models based on multi-center data. Future research should focus on developing customized models for local use, enhancing the accuracy and reliability of glioblastoma segmentation in a context where dataset shift is inevitable. Additionally, the study emphasizes the need for further investigation into the impact of covariate shift on model performance and the development of methods to mitigate its effects.
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