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Mammography images separated into lipid, water, and protein.

Fig. 2: 3CB, lipid, water, and protein, data, and regions of feature extraction. (2021). Leong LT, Malkov S, Drukker K, Niell BL, Sadowski P, Wolfgruber T, et al. Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions.

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Figure from Willingham 2021 publication

ROC melanoma vs non-melanoma 70% trained, 20% validate, 10% test. (2021). Willingham Jr ML, Spencer SY, Lum CA, Sanchez JMN, Burnett T, Shepherd J, et al. The potential of using artificial intelligence to improve skin cancer diagnoses in Hawai‘i’s multiethnic population.

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Figure from Hinton 2019 publication

Loss and accuracy curves per epoch of the test and train set of the deep learning network. Best test loss occurred on epoch 482. At that epoch training loss and accuracy were 0.58 and 67.4%, respectively, and test loss and accuracy were 0.499 and 75.2%, respectively. (2019). Hinton B, Ma L, Mahmoudzadeh AP, Malkov S, Fan B, Greenwood H, et al. Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

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