AWGUNET: Attention-Aided Wavelet Guided U-Net for Nuclei Segmentation in Histopathology Images

TL;DR

This paper introduces AWGUNET, a novel deep learning model combining U-Net, DenseNet-121, wavelet-guided attention, and global attention modules to improve nuclei segmentation accuracy in histopathology images, aiding cancer diagnosis.

Contribution

The paper presents a new nuclei segmentation model that integrates wavelet-guided and global attention modules with U-Net and DenseNet-121, enhancing boundary detection and contextual understanding.

Findings

Outperforms existing models on Monuseg and TNBC datasets.

Effectively captures complex cell boundaries and staining variations.

Demonstrates potential for improved cancer diagnosis through better segmentation.

Abstract

Accurate nuclei segmentation in histopathological images is crucial for cancer diagnosis. Automating this process offers valuable support to clinical experts, as manual annotation is time-consuming and prone to human errors. However, automating nuclei segmentation presents challenges due to uncertain cell boundaries, intricate staining, and diverse structures. In this paper, we present a segmentation approach that combines the U-Net architecture with a DenseNet-121 backbone, harnessing the strengths of both to capture comprehensive contextual and spatial information. Our model introduces the Wavelet-guided channel attention module to enhance cell boundary delineation, along with a learnable weighted global attention module for channel-specific attention. The decoder module, composed of an upsample block and convolution block, further refines segmentation in handling staining patterns. The experimental results conducted on two publicly accessible histopathology datasets, namely Monuseg and TNBC, underscore the superiority of our proposed model, demonstrating its potential to advance histopathological image analysis and cancer diagnosis. The code is made available at: https://github.com/AyushRoy2001/AWGUNET.