# MicroMIL: Graph-Based Multiple Instance Learning for Context-Aware Diagnosis with Microscopic Images Source: [MicroMIL: Graph-Based Multiple Instance Learning for Context-Aware Diagnosis with Microscopic Images]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:54:36.998Z Authors: Jongwoo Kim, Bryan Wong, Huazhu Fu, Willmer Rafell Qui\\~nones, Youngsin Ko, Mun Yong Yi Published: 2024-07-31T13:38:47.000Z ## Abstract Cancer diagnosis has greatly benefited from the integration of whole-slide images (WSIs) with multiple instance learning (MIL), enabling high-resolution analysis of tissue morphology. Graph-based MIL (GNN-MIL) approaches have emerged as powerful solutions for capturing contextual information in WSIs, thereby improving diagnostic accuracy. However, WSIs require significant computational and infrastructural resources, limiting accessibility in resource-constrained settings. Conventional light microscopes offer a cost-effective alternative, but applying GNN-MIL to such data is challenging due to extensive redundant images and missing spatial coordinates, which hinder contextual learning. To address these issues, we introduce MicroMIL, the first weakly-supervised MIL framework specifically designed for images acquired from conventional light microscopes. MicroMIL leverages a representative image extractor (RIE) that employs deep cluster embedding (DCE) and hard Gumbel-Softmax to dynamically reduce redundancy and select representative images. These images serve as graph nodes, with edges computed via cosine similarity, eliminating the need for spatial coordinates while preserving contextual information. Extensive experiments on a real-world colon cancer dataset and the BreakHis dataset demonstrate that MicroMIL achieves state-of-the-art performance, improving both diagnostic accuracy and robustness to redundancy. The code is available at https://github.com/kimjongwoo-cell/MicroMIL ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on BreakHis](): Image Classification; BreakHis; Accuracy: 96.43. - [Image Classification on Real-world Seegene](): Image Classification; Real-world Seegene; Accuracy: 99.22. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()