monte-carlo-dropout-e0d4ffea·2 events·first seen Aliases: Monte Carlo Dropout
A preprint from arXiv evaluates Monte Carlo Dropout for voxel-level uncertainty estimation in glioma segmentation on 126 BraTS21 patients, comparing a pretrained SegResNet and a locally trained UNet-Res. While global uncertainty-error alignment is strong (AUROC ~0.97), the study finds that UNet-Res exhibits near-zero entropy and an ECE of 0.915 on the enhancing tumour sub-region despite a Dice of only 0.714, a severe miscalibration invisible to standard Dice and AUROC metrics. The paper argues that sub-region-specific calibration assessment is necessary for clinical safety and cannot be replaced by aggregate metrics alone.
The Thaka team describes their winning system for Task 2 of the KSAA-2026 Shared Task on Arabic Speech Dictation with Automatic Diacritization, which requires producing fully diacritized Arabic text from speech audio and undiacritized transcripts. Their approach fine-tunes CATT-Whisper, a multimodal model combining a CATT text encoder with a frozen Whisper speech encoder, under severe data constraints (2,327 training samples, no external data). Key techniques include R-Drop consistency regularization, Optuna-optimized hyperparameters with high weight decay, Focal Loss, and Monte Carlo Dropout inference averaging over 200 stochastic forward passes across four checkpoints. The system achieves 23.26% WER on the primary metric, placing first among all participants.