MicroMIL: Graph-based Contextual Multiple Instance Learning for Patient Diagnosis Using Microscopy Images

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 spatial and relational structures in WSIs, thereby improving diagnostic accuracy. However, despite their effectiveness, WSIs require significant computational and infrastructural resources, limiting accessibility in resource-constrained settings. Microscopy imaging provides a cost-effective alternative, but applying GNN-MIL to microscopy imaging is challenging due to the absence of spatial coordinates and the high redundancy in pathologist-acquired images. To address these issues, we introduce MicroMIL, the first weakly-supervised MIL framework specifically designed for microscopy imaging. 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 selected images serve as graph nodes, with edges determined by cosine similarity, eliminating the need for spatial coordinates while preserving relational structure. 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 atthis https URL

@article{kim2025_2407.21604,
  title={ MicroMIL: Graph-based Contextual Multiple Instance Learning for Patient Diagnosis Using Microscopy Images },
  author={ JongWoo Kim and Bryan Wong and Huazhu Fu and Willmer Rafell Quiñones and MunYong Yi },
  journal={arXiv preprint arXiv:2407.21604},
  year={ 2025 }
}