# MAP-SCTNet: multi-scale pyramid and frequency-enhanced network for colorectal cancer histopathological image segmentation

## TL;DR

The paper introduces MAP-SCTNet, a new network for segmenting colorectal cancer histopathological images that improves accuracy and efficiency.

## Contribution

MAP-SCTNet introduces three innovations: MS-ASPP, AFE-TAM, and PDTKD for better segmentation performance with fewer resources.

## Key findings

- MAP-SCTNet achieves a mean Dice coefficient of 83.76% and mean IoU of 74.5% on the EBHI-SEG dataset.
- The model reduces parameters by 79.3% and computation by 71.1% compared to TransUNet.

## Abstract

Medical image segmentation remains challenging due to the morphological heterogeneity of lesions, inherently low contrast with ambiguous boundaries, and limited availability of annotated training data. Existing methods often struggle to simultaneously capture multi-scale contextual information and preserve fine-grained boundary details, particularly in resource-constrained clinical settings. To address these challenges, we propose MAP-SCTNet, an efficient segmentation network that enhances the SCTNet architecture through three synergistic innovations. First, we design a Multi-Scale Atrous Spatial Pyramid Pooling (MS-ASPP) module that employs five parallel dilated convolution branches coupled with a dual-attention mechanism and boundary-aware refinement, enabling comprehensive multi-scale feature extraction while maintaining precise edge localization. Second, we introduce an Adaptive Frequency Enhancement and Texture-Aware Module (AFE-TAM) that operates in the frequency domain to adaptively fuse low, mid, and high-frequency components, complemented by Gabor filtering and Local Binary Patterns for robust texture representation across pathological subtypes. Third, we develop a Progressive Dual-Teacher Knowledge Distillation (PDTKD) framework that leverages both Transformer-based and CNN-based teacher networks through a three-stage training strategy, effectively bridging the gap between training and inference while improving generalization from limited data. Extensive experiments on the EBHI-SEG colorectal cancer histopathological dataset, which comprises biopsy specimens collected via colonoscopy, demonstrate that MAP-SCTNet achieves state-of-the-art performance with a mean Dice coefficient of 83.76% and mean IoU of 74.5%, surpassing recent methods including TransUNet, Swin-UNet, and SegNeXt. Notably, our model requires only 21.8M parameters and 30.7G FLOPs, representing a 79.3% reduction in parameters and 71.1% reduction in computation compared to TransUNet, making it well-suited for real-time computer-aided diagnosis in clinical colorectal cancer diagnosis workflows.