# Semi-Supervised Learning

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.

The work proposes PC-Seg (Progressive Cross-view Segmentation), a five-stage curriculum learning framework in which a single 2D model first learns cross-view consistency between standard B-scans and orthogonal slices to generate reliable volumetric pseudo-labels, which are then distilled into a 3D model and followed by 2D/3D co-training with ensemble pseudo-labeling; on the public MSHC and Duke DME OCT datasets it reaches segmentation accuracy comparable to fully supervised learning using only about 0.7% of the labeled data, outperforming the semi-supervised and retinal layer segmentation methods it compares against.

The work proposes PC-Seg (Progressive Cross-view Segmentation), a five-stage curriculum learning framework in which a single 2D model first learns cross-view consistency between standard B-scans and orthogonal slices to generate reliable volumetric pseudo-labels, which are then distilled into a 3D model and followed by 2D/3D co-training with ensemble pseudo-labeling; on the public MSHC and Duke DME OCT datasets it reaches segmentation accuracy comparable to fully supervised learning using only about 0.7% of the labeled data, outperforming the semi-supervised and retinal layer segmentation methods it compares against.

Using 1,825,948 chemotherapy appointment records from the Dana-Farber Cancer Institute, this study builds a hierarchical machine learning pipeline that first predicts cancellations and then no-shows, achieving F1-scores of 0.76 and 0.82 and improving minority-class performance by 7–10 points over a single-stage multinomial baseline; it further uses semi-supervised learning to infer no-show reasons from short-notice cancellations (weighted F1 of 0.57 and 0.54) and applies counterfactual simulation to evaluate interventions, finding that standard reminders are less effective than previously reported while provider consistency and commitment-based scheduling can reduce cancellations and no-shows.

Using 1,825,948 chemotherapy appointment records from the Dana-Farber Cancer Institute, this study builds a hierarchical machine learning pipeline that first predicts cancellations and then no-shows, achieving F1-scores of 0.76 and 0.82 and improving minority-class performance by 7–10 points over a single-stage multinomial baseline; it further uses semi-supervised learning to infer no-show reasons from short-notice cancellations (weighted F1 of 0.57 and 0.54) and applies counterfactual simulation to evaluate interventions, finding that standard reminders are less effective than previously reported while provider consistency and commitment-based scheduling can reduce cancellations and no-shows.