  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.
