Histopathologic analysis is a key confirmatory step in the cancer diagnosis pipeline, where pathologists analyze a tissue section of interest for abnormalities and the extent of disease progression. The digitization of these tissue slides has enabled the use of AI for Whole Slide Image (WSI) Analysis, primarily to simplify pathologists’ laborious tasks and improve diagnostic accuracy. Due to the large size of these images, they’re split into smaller patches, and after analysis of individual patches, the results are aggregated to get the result for the WSI. Such a paradigm is called Multiple Instance Learning (MIL).

Architectural patterns surrounding the tumor regions are key indicators of angiogenesis and help in prognosis prediction. Architectural patterns are classified into 9 types and are grouped into three categories based on the underlying vasculature. While some patterns are often seen, others are rare and common only in higher-grade tumors. This causes a data imbalance that may affect training. To overcome this challenge, we propose an ensemble classifier for architectural pattern classification.

To capture local details and global context, we employ a multi-resolution feature encoder. At each resolution, the Saab transform is applied to obtain joint spatial-spectral representations. Representation learning is followed by a pooling operation to obtain compact representations. The pooled features from each resolution are concatenated to obtain a single feature vector, which is used to select the most discriminant features for the target classification task. An XGBoost binary classifier trained on these selected features predicts a confidence score for each architectural pattern. The confidence scores from multiple one-vs-one classifiers are aggregated to predict the architectural pattern in each patch.