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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Mobile-PolypNet: Lightweight Colon Polyp Segmentation Network for Low-Resource Settings.

Ranit Karmakar¹, Saeid Nooshabadi¹

¹Electrical and Computer Engineering, Michigan Technological University, Houghton, MI 49931, USA.

Journal of Imaging

|June 23, 2022

Summary

This summary is machine-generated.

This study introduces a lightweight deep learning model for efficient colorectal polyp segmentation, crucial for preventing colorectal cancer (CRC). The model achieves state-of-the-art accuracy with significantly reduced computational resources and size.

Keywords:

colorectal cancer deep learning polyp segmentation

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Area of Science:

Medical Imaging and Artificial Intelligence
Computational Pathology
Oncology

Background:

Colorectal cancer (CRC) is a leading global cancer, with early polyp detection being critical for prevention.
Existing deep learning models for colorectal polyp segmentation are computationally intensive and resource-heavy.
There is a need for efficient and accurate automated polyp detection systems.

Purpose of the Study:

To develop a lightweight deep learning model for accurate colorectal polyp segmentation.
To significantly reduce model size, complexity, and computational requirements compared to existing methods.
To evaluate the model's performance on multiple public datasets.

Main Methods:

A novel lightweight deep learning autoencoder model was designed using advanced architectural blocks and optimization techniques.
The model was trained and validated on five diverse colorectal polyp segmentation datasets: CVC-ClinicDB, CVC-ColonDB, EndoScene, Kvasir, and ETIS.
Ablation studies were conducted to assess the impact of specific architectural components, and performance was evaluated using standard image segmentation metrics.

Main Results:

The proposed model achieved state-of-the-art DICE scores of 0.935 (Kvasir) and 0.945 (CVC-ClinicDB), outperforming existing methods.
The model demonstrated remarkable efficiency, utilizing 88x fewer parameters, 40x less storage, and being 17x more computationally efficient.
Ablation testing indicated that adding ConvSkip did not significantly impact performance (p=0.815).

Conclusions:

The developed lightweight deep learning autoencoder offers a highly accurate and efficient solution for colorectal polyp segmentation.
This model presents a significant advancement in resource-constrained environments for early CRC detection and prevention.
The findings support the deployment of efficient AI tools in clinical settings for improved diagnostic capabilities.