【盈科AI】ChatModels 多模型互动平台四大能力全新升级

图注信息：Figure 1 im4MEC deep learning pipeline Hide captionFigure viewer For a high-resolution version of this image see appendix 2. (A) Whole-slide images were segmented and cut into non-overlapping square tiles of 360 μm at 40× magnification and resized to 224 × 224 pixels. (B) The optimal number of tiles were sampled from each whole-slide image to build a training dataset for the MoCo-v2 self-supervised learning model. (C) Features were extracted from all tiles of the whole-slide image by use of the self-supervised learning encoder, ResNet-50, at the last layer resulting in features of size 2048. (D) The model was trained to molecularly classify the whole-slide image, assigning attention scores to each tile and molecular class. Attention heatmaps are displayed from low attention (blue) to high attention (red). (E) The top 20 attended tiles were extracted from the predicted attention branch only. The predictions of HoVer-Net, a nuclear segmentation and classification deep learning model trained on an endometrial cancer tile image dataset, were used to compute counts of the three cell types and size and shape of the tumour nuclei (appendix 1 p 29). Subsequent analyses described these morphological features in association with the molecular classes and measured their relative feature importance with a support vector machine. MMRd=mismatch repair protein deficient. NSMP=no specific molecular profile. p53abn=abnormal cellular tumour antigen p53 expression.

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