# Deep Ensembles

With voxel-level annotations on only 10% of CT scans, a heterogeneous ensemble of four independently trained models was applied to candidate-centred segmentation of 3–<6 mm pulmonary nodules. Against a single enhanced Mean Teacher, it raised Dice by 0.01488 on an internal held-out set and by 0.01118 on external LNDb without fine-tuning, with both confidence intervals excluding zero and concordant directions across three training seeds. The gain came with higher recall and lower precision, and did not establish that heterogeneous objectives beat homogeneous four-model ensembles.

With voxel-level annotations on only 10% of CT scans, a heterogeneous ensemble of four independently trained models was applied to candidate-centred segmentation of 3–<6 mm pulmonary nodules. Against a single enhanced Mean Teacher, it raised Dice by 0.01488 on an internal held-out set and by 0.01118 on external LNDb without fine-tuning, with both confidence intervals excluding zero and concordant directions across three training seeds. The gain came with higher recall and lower precision, and did not establish that heterogeneous objectives beat homogeneous four-model ensembles.

This study used a Leave-One-Class-Out out-of-distribution detection setup in motor imagery BCIs, training a model on some classes and observing whether an unfamiliar movement class can be detected via increased uncertainty; it found that because of the high intrinsic variability of EEG signals, many users show higher uncertainty for familiar in-distribution classes than for out-of-distribution classes, so many OOD detection methods that perform well in other machine learning domains prove ineffective here, yet OOD detection performance correlates with on-task performance, and Deep Ensemble and MC-Dropout models achieved on-task AUROC above 0.9 and OOD detection ability up to about 0.7, showing that rejecting unfamiliar cognitive states becomes feasible when task performance is high.

This study used a Leave-One-Class-Out out-of-distribution detection setup in motor imagery BCIs, training a model on some classes and observing whether an unfamiliar movement class can be detected via increased uncertainty; it found that because of the high intrinsic variability of EEG signals, many users show higher uncertainty for familiar in-distribution classes than for out-of-distribution classes, so many OOD detection methods that perform well in other machine learning domains prove ineffective here, yet OOD detection performance correlates with on-task performance, and Deep Ensemble and MC-Dropout models achieved on-task AUROC above 0.9 and OOD detection ability up to about 0.7, showing that rejecting unfamiliar cognitive states becomes feasible when task performance is high.