HI-Net: A novel histopathologic image segmentation model for metastatic breast cancer via lightweight dataset construction

Abstract

Since 2020, breast cancer has remained the most prevalent cancer worldwide and the World Health Organisation projects significant increases by 2040, with new cases expected to exceed 3 million annually (a 40% increase) and deaths to surpass 1 million (a 50% increase), highlighting the urgent need for advancements in detection and treatment. Current detection of metastasis is highly dependent on labour-intensive and error-prone pathological examination of large-scale biotissue. Given the high-resolution (100,000 × 100,000 gigapixels) but limited quantity of open-source pathological slide datasets, existing deep learning models face preprocessing challenges. This paper introduces HI-Net, a high-speed panoramic feature-extraction pyramid network for rapid and accurate detection of metastatic breast cancer, balancing panoramic segmentation and local attention. Additionally, a lightweight pathological slide dataset optimised for 512 x 512-pixel resolution, derived from downsampled and reassembled competitive datasets, accelerates training and reduces computational costs. HI-Net demonstrates superior performance on existing medical imaging competition datasets and our lightweight dataset, evidencing its effectiveness across datasets and potential for contributing to the generalisation of intelligent diagnostics.

Keywords

• Breast cancer
• Histopathology slide
• Deep learning
• Medical imaging
• Intelligent diagnosis