A Novel Convolution Transformer-Based Network for Histopathology Image Classification Using Adaptive Convolution and Dynamic Attention

This repository contains the official implementation of our paper, "A Novel Convolution Transformer-Based Network for Histopathology Image Classification Using Adaptive Convolution and Dynamic Attention". Our model combines the benefits of convolutional neural networks (CNNs) and transformers, utilizing innovative adaptive convolution and dynamic attention mechanisms for enhanced feature extraction and improved classification of histopathology images.

Datasets

We evaluated our model on multiple histopathology datasets. Experiments were conducted on four publicly available histopathology datasets:

• Kasturba Medical College dataset (KMC)
• Colorectal cancer histology (CRCH)
• Breast cancer histology (BreakHis)
• Colon cancer histopathology dataset (CCH)

Comparison

Comparison of the performance between the proposed model and other state-of-the-art models:

Citation

If you use this work in your research, please cite the following paper:

"A Novel Convolution Transformer-Based Network for Histopathology Image Classification Using Adaptive Convolution and Dynamic Attention" Engineering Applications of Artificial Intelligence, Volume 135, September 2024, 108824