Imaging and Radiation Research
Transferability of cost-sensitive threshold policies in pneumoniamnist: Calibration, resampling, and image-integrity sensitivity
By Arvy Noleal Osma and Mark Andre Balao.
Abstract
Background
Validation-selected thresholds may fail to retain their apparent error-cost advantage across imaging benchmark partitions, even with satisfactory discrimination.
Objective
To examine transferability of fixed, analytical cost-based, and validation-selected classification policies in PneumoniaMNIST, with threshold-selection uncertainty and sensitivity analyses.
Methods
We analyzed 5,856 28 × 28 pediatric chest radiographs (4,708 training; 524 source validation; 624 testing). Eight exact train-validation overlaps were excluded from validation procedures. A 100-component logistic baseline and compact convolutional neural network (CNN) were evaluated under four hypothetical false-negative/false-positive cost ratios. Revision-stage analyses included 500 validation-threshold resamples, 1,000 paired test resamples, exact-score threshold searches, class-composition standardization, calibration diagnostics, three CNN seeds, training-duplicate removal, and a development-role-separated sensitivity experiment.