Fig. 1: Overview of our multiple instance learning models. | Nature Communications

Fig. 1: Overview of our multiple instance learning models.

From: Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer

Fig. 1

Patches of varying magnifications (5× and 20×) were extracted from the whole-slide images (WSIs). The patches were then processed using automated cancer segmentation to exclude patches without cancer cells and fed into a contrastive self-supervised learning algorithm (blue arrow path). Alternatively, all patches, including those without cancer cells, could be fed directly into the self-supervised learning algorithm to include all tissues in the WSIs (red arrow path). Separate multiple instance learning (MIL) methods were used for two single scales and one multiscale magnification setting (5×, 20×, and both) for each image area. Therefore, six different MIL models were generated. For the multiscale MILs, feature pyramids were formed by concatenating the embeddings of different scales of WSIs to train the MIL aggregator.

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