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Factorizer: A scalable interpretable approach to context modeling for medical image segmentation.

Pooya Ashtari¹, Diana M Sima², Lieven De Lathauwer³

¹Department of Electrical Engineering (ESAT), STADIUS Center, KU Leuven, Leuven, Belgium; CREATIS (CNRS UMR5220 & INSERM U1294), Université Claude Bernard Lyon 1, Lyon, France.

Medical Image Analysis

|December 14, 2022

Summary

This study introduces Factorizer models for medical image segmentation, achieving state-of-the-art results in brain tumor and stroke lesion segmentation. Factorizers offer improved accuracy, scalability, and interpretability compared to existing Convolutional Neural Networks and Transformers.

Keywords:

Matrix factorization Medical image segmentation U-Net Vision transformer

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Published on: January 7, 2019

Area of Science:

Medical image analysis
Computer vision
Machine learning

Background:

Convolutional Neural Networks (CNNs) excel in medical image segmentation but struggle with global context due to convolution's locality.
Transformers show promise in vision tasks but their quadratic attention complexity limits high-resolution context modeling.
Existing methods face challenges in balancing global context capture with computational efficiency for medical image segmentation.

Purpose of the Study:

To introduce a novel family of models, Factorizers, for end-to-end medical image segmentation.
To address the limitations of CNNs and Transformers in capturing global context and computational scalability.
To develop a linearly scalable approach for context modeling in medical image segmentation.

Main Methods:

Proposed a U-shaped architecture integrating a differentiable Nonnegative Matrix Factorization (NMF) layer for linearly scalable context modeling.
Utilized the shifted window technique in conjunction with NMF to effectively aggregate local information.
Developed Factorizers, a novel model family leveraging low-rank matrix factorization for segmentation tasks.

Main Results:

Factorizers achieved state-of-the-art performance on the BraTS (brain tumor) and ISLES'22 (stroke lesion) datasets.
Demonstrated superior accuracy, scalability, and interpretability compared to CNNs and Transformers.
NMF components provided enhanced interpretability over existing models.
Achieved significant inference speed-up without substantial accuracy loss.

Conclusions:

Factorizers represent a promising advancement in medical image segmentation, outperforming current state-of-the-art methods.
The proposed NMF-based approach offers a scalable, interpretable, and efficient solution for complex segmentation tasks.
Factorizers provide a unique advantage in terms of inference speed, making them highly practical for clinical applications.