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Fig. 2 | Journal of Hematology & Oncology

Fig. 2

From: AMLnet, A deep-learning pipeline for the differential diagnosis of acute myeloid leukemia from bone marrow smears

Fig. 2

Performance of the AMLnet compared with junior and senior pathologists and gradient visualizations of the AMLnet using the integrated gradient algorithm. a Workflow of the AMLnet versus pathologists’ performance study. b, c The chart on the left indicates the mean coverage of the prediction results for all the patients we provided, and the chart on the right is the comparison between pathologists and AMLnet only with different patients selected for certain predictions by the different pathologists. d Saliency maps are used to illustrate the gradient of a pixel with respect to the AMLnet’s loss function. Brighter pixels have a greater influence on AMLnet’s classification decision. The scale bar from blue to red indicates the increased contribution of the location to the model's classification choice. These maps suggest that the network learns to focus on the leukocyte and maps out its internal structures while giving less weight to background content. The columns are (1) the original image, (2) a saliency map, and (3) a saliency map overlaying the original image. Rather than equally weighting all AML-related cells, our AMLnet discriminates against them. The saliency maps for M4Eo indicate that our AMLnet only considered myelomonocytic with eosinophils as an essential foundation for assessment when predicting M4Eo compared to other granulocytes, and the maps for M7 indicate that only megakaryocytes were considered

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