Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction

Faust, K; Xie, Q; Han, D; Goyle, K; Volynskaya, Z; Djuric, U; Diamandis, P

AI Collection

Journal article

Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction

Abstract:: Commonly used AI networks are very self-confident in their predictions, even when the evidence for a certain decision is dubious. The investigation of a deep learning model output is pivotal for understanding its decision processes and assessing its capabilities and limitations. By analyzing the distributions of raw network output vectors, it can be observed that each class has its own decision boundary and, thus, the same raw output value has different support for different classes. Inspired by this fact, we have developed a new method for out-of-distribution detection. The method offers an explanatory step beyond simple thresholding of the softmax output towards understanding and interpretation of the model learning process and its output. Instead of assigning the class label of the highest logit to each new sample presented to the network, it takes the distributions over all classes into consideration. A probability score interpreter (PSI) is created based on the joint logit values in relation to their respective correct vs wrong class distributions. The PSI suggests whether the sample is likely to belong to a specific class, whether the network is unsure, or whether the sample is likely an outlier or unknown type for the network. The simple PSI has the benefit of being applicable on already trained networks. The distributions for correct vs wrong class for each output node are established by simply running the training examples through the trained network. We demonstrate our OOD detection method on a challenging transmission electron microscopy virus image dataset. We simulate a real-world application in which images of virus types unknown to a trained virus classifier, yet acquired with the same procedures and instruments, constitute the OOD samples

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Faust, K., Xie, Q., Han, D., Goyle, K., Volynskaya, Z., Djuric, U., & Diamandis, P. (2018). Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction. BMC Bioinformatics, 19(1), 173–173.

MLA Style

Faust, K, et al. “Visualizing Histopathologic Deep Learning Classification and Anomaly Detection Using Nonlinear Feature Space Dimensionality Reduction.” BMC Bioinformatics, vol. 19, no. 1, 2018, pp. 173–73.

Chicago Style

Faust, K, Q Xie, D Han, et al. 2018. “Visualizing Histopathologic Deep Learning Classification and Anomaly Detection Using Nonlinear Feature Space Dimensionality Reduction.” BMC Bioinformatics 19 (1): 173–73.
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