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Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

9 Apr 2022arXiv:2204.04516archive 2025-07-28

James M Dolezal, Andrew Srisuwananukorn, Dmitry Karpeyev, Siddhi Ramesh, Sara Kochanny, Brittany Cody, Aaron Mansfield, Sagar Rakshit, Radhika Bansa, Melanie Bois, Aaron O Bungum, Jefree J Schulte, Everett E Vokes, Marina Chiara Garassino, Aliya N Husain, Alexander T Pearson

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a novel, clinically-oriented approach to uncertainty quantification (UQ) for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.

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batch_loss_crossentropy jamesdolezal/slideflow/slideflow/model/tensorflow_utils.py official repository unverified Apache-2.0 (permissive) · 383daf686e96763a · report
calculate_heatmap_extent jamesdolezal/slideflow/slideflow/heatmap.py official repository unverified Apache-2.0 (permissive) · 15f7d45767c330f3 · report
convert_dataset jamesdolezal/slideflow/slideflow/model/adv_utils.py official repository unverified Apache-2.0 (permissive) · 7d98ff8e54bdaca6 · report
get_layer_index_by_name jamesdolezal/slideflow/slideflow/model/tensorflow_utils.py official repository unverified Apache-2.0 (permissive) · 84b9fad938f056de · report
get_module_by_name jamesdolezal/slideflow/slideflow/model/torch_utils.py official repository unverified Apache-2.0 (permissive) · 09dddb2a3dd86eec · report
get_uq_predictions jamesdolezal/slideflow/slideflow/model/torch_utils.py official repository unverified Apache-2.0 (permissive) · 61499d2ec0c1fa05 · report
negative_log_likelihood jamesdolezal/slideflow/slideflow/model/tensorflow_utils.py official repository unverified Apache-2.0 (permissive) · db044050ec123a2d · report
print_module_summary jamesdolezal/slideflow/slideflow/model/torch_utils.py official repository unverified Apache-2.0 (permissive) · 4b76772761f0cc1a · report
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AttributeUncertainty Quantificationwhole slide images

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1x1 ConvolutionAverage PoolingConvolutionDense ConnectionsDepthwise ConvolutionDepthwise Separable ConvolutionDropoutGlobal Average PoolingMax PoolingPointwise ConvolutionReLUResidual ConnectionSoftmax

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