FourierMIL: Fourier filtering-based multiple instance learning for whole slide image analysis

Recent advancements in computer vision, driven by convolutional neural network, multilayer perceptron and transformer architectures, have significantly improved the analysis on natural images. Despite their potential, the application of these architectures in digital pathology, specifically for analyzing gigapixel-resolution whole-slide images (WSIs), remains challenging due to the extensive and variable sizes of these images. Here we present a multiple instance learning framework that leverages the discrete Fourier transform and learns from WSIs. Dubbed as FourierMIL, our framework is designed to capture both global and local dependencies within WSIs. To validate the efficacy of our model, we conducted extensive experiments on a prevalent computational pathology challenge: tumor classification. Our results demonstrate that FourierMIL outperforms existing state-of-the-art methods, marking a significant advancement in the field of digital pathology and highlighting the potential of attention-free architectures in managing the complexities related to WSI analysis. The code will be released for public access upon the manuscripts acceptance.