OCTID: a one-class learning-based Python package for tumor image detection

Yanan Wang, Litao Yang, Geoff Webb, Zongyuan Ge, Jiangning Song

Research output: Contribution to journalArticleOtherpeer-review

10 Citations (Scopus)


Motivation: Tumor tile selection is a necessary prerequisite in patch-based cancer whole slide image analysis, which is labor-intensive and requires expertise. Whole slides are annotated as tumor or tumor free, but tiles within a tumor slide are not. As all tiles within a tumor free slide are tumor free, these can be used to capture tumor-free patterns using the one-class learning strategy. Results: We present a Python package, termed OCTID, which combines a pretrained convolutional neural network (CNN) model, Uniform Manifold Approximation and Projection (UMAP) and one-class support vector machine to achieve accurate tumor tile classification using a training set of tumor free tiles. Benchmarking experiments on four H&E image datasets achieved remarkable performance in terms of F1-score (0.90 ± 0.06), Matthews correlation coefficient (0.93 ± 0.05) and accuracy (0.94 ± 0.03).

Original languageEnglish
Pages (from-to)3986-3988
Number of pages3
Issue number21
Publication statusPublished - 1 Nov 2021

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