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Published on: April 28, 2022
Involution-based efficient autoencoder for denoising histopathological images with enhanced hybrid feature
Md Farhadul Islam1, Md Tanzim Reza2, Meem Arafat Manab3
1Computing for Sustainability and Social Good (C2SG) Research Group, Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh; Department of Computer Science and Engineering, School of Data and Sciences, BRAC University, Dhaka, Bangladesh.
A new lightweight autoencoder effectively denoises histopathology images, crucial for accurate disease analysis. This efficient model significantly reduces computational costs while preserving vital spatial features, outperforming existing methods.
Area of Science:
- Medical Imaging
- Computational Pathology
- Artificial Intelligence
Background:
- Histopathology image noise, stemming from hardware, preparation, and environment, hinders disease analysis.
- Existing denoising models often fail to capture spatial features and are computationally intensive.
- Sparse spatial features in histopathology images are critical for diagnosis but can be degraded by denoising.
Purpose of the Study:
- To develop an efficient and precise denoising method for histopathology images.
- To address the limitations of current models in extracting spatial features and computational cost.
- To create a lightweight autoencoder capable of preserving critical diagnostic information.
Main Methods:
- Proposed a lightweight autoencoder (43.11 KB) by integrating an involution layer into a small convolutional model.
- Employed a hybrid approach for channel-specific and location-specific feature extraction.
- Evaluated performance using SSIM Loss and Peak-Signal-to-Noise-Ratio on Malaria Blood Smear and CRC datasets.
Main Results:
- Achieved low SSIM Loss (0.058 and 0.34) when denoising images with Gaussian noise (0.3).
- The autoencoder has minimal weight parameters (11,037) and FLOPs (81,630,000).
- Demonstrated over 20x greater computational efficiency compared to the next best model (Xception).
Conclusions:
- The proposed lightweight autoencoder offers superior denoising performance for histopathology images.
- The model provides significant reductions in memory and computational overhead.
- This method represents a highly efficient solution for histopathology image denoising, minimizing artifact generation.
