Proportion constrained weakly supervised histopathology image classification

• Published in: Computers in biology and medicine 2022-08, Vol.147, p.105714-105714, Article 105714
• Main Authors: Silva-Rodríguez, Julio, Schmidt, Arne, Sales, María A., Molina, Rafael, Naranjo, Valery
• Format: Article
• Language: English
• Subjects: Classification; Constraints; Datasets; Extended log-barrier; Histology; Histopathology; Image analysis; Image classification; Image processing; Inequality constraints; Medical imaging; Multiple instance learning; Optimization; Proportion; Prostate cancer; Supervised learning
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.105714

Multiple instance learning (MIL) deals with data grouped into bags of instances, of which only the global information is known. In recent years, this weakly supervised learning paradigm has become very popular in histological image analysis because it alleviates the burden of labeling all cancerous regions of large Whole Slide Images (WSIs) in detail. However, these methods require large datasets to perform properly, and many approaches only focus on simple binary classification. This often does not match the real-world problems where multi-label settings are frequent and possible constraints must be taken into account. In this work, we propose a novel multi-label MIL formulation based on inequality constraints that is able to incorporate prior knowledge about instance proportions. Our method has a theoretical foundation in optimization with log-barrier extensions, applied to bag-level class proportions. This encourages the model to respect the proportion ordering during training. Extensive experiments on a new public dataset of prostate cancer WSIs analysis, SICAP-MIL, demonstrate that using the prior proportion information we can achieve instance-level results similar to supervised methods on datasets of similar size. In comparison with prior MIL settings, our method allows for ∼13% improvements in instance-level accuracy, and ∼3% in the multi-label mean area under the ROC curve at the bag-level. •A novel constrained formulation for weakly supervised histology WSI classification.•Using relative class proportions priors for prostate cancer instances grading.•SICAP-MIL, a novel publicly available dataset of prostate WSIs for MIL benchmark.