{"id":1971,"date":"2026-01-28T15:20:59","date_gmt":"2026-01-28T15:20:59","guid":{"rendered":"https:\/\/aequip.it\/?post_type=articoli&p=1971"},"modified":"2026-01-28T15:20:59","modified_gmt":"2026-01-28T15:20:59","slug":"a-dynamic-uncertainty-aware-ensemble-model-application-to-lung-cancer-segmentation-in-digital-pathology","status":"publish","type":"articoli","link":"https:\/\/aequip.it\/en\/papers\/a-dynamic-uncertainty-aware-ensemble-model-application-to-lung-cancer-segmentation-in-digital-pathology\/","title":{"rendered":"A dynamic uncertainty-aware ensemble model: Application to lung cancer segmentation in digital pathology"},"excerpt":{"rendered":"

Ensemble models have emerged as a powerful technique for improving robustness in medical image segmentation. However, traditional ensembles suffer from limitations such as under-confidence and over-reliance on poor performing models. In this work, we introduce an Adaptive Uncertainty-based Ensemble (AUE) model for tumor segmentation in histopathological slides.<\/p>\n","protected":false},"featured_media":1970,"template":"","class_list":["post-1971","articoli","type-articoli","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nA dynamic uncertainty-aware ensemble model: Application to lung cancer segmentation in digital pathology - Aequip<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/aequip.it\/en\/papers\/a-dynamic-uncertainty-aware-ensemble-model-application-to-lung-cancer-segmentation-in-digital-pathology\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A dynamic uncertainty-aware ensemble model: Application to lung cancer segmentation in digital pathology - Aequip\" \/>\n<meta property=\"og:description\" content=\"Ensemble models have emerged as a powerful technique for improving robustness in medical image segmentation. 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