ABMIL
PulseAugur coverage of ABMIL — every cluster mentioning ABMIL across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
-
New survival model validation method shows cohort-dependent performance
Researchers have developed and validated a method called drcosarc, a post-hoc conformal wrapper for discrete-time multiple-instance learning survival models. This method was tested across multiple cohorts from The Cance…
-
New SAGE framework offers semantic explanations for AI in pathology
Researchers have developed SAGE, a new post-hoc framework designed to provide semantic, language-grounded explanations for attention-based multiple instance learning (ABMIL) models used in computational pathology. Unlik…
-
New AI method uses patient labels to train cancer registry models
Researchers have developed a novel framework using Attention-Based Multiple Instance Learning (ABMIL) to train deep learning models for cancer registry tasks without requiring individual report annotations. This method …
-
Digital pathology study finds tile-level AI benchmarks predict slide-level performance
A new study published on arXiv explores the efficiency of using tile-level performance as a proxy for slide-level outcomes in digital pathology. Researchers benchmarked 19 foundation models across 42 slide-level and 16 …
-
Foundation models show modest gains for whole-slide image retrieval in cancer data
A new study published on arXiv evaluates ten different pipelines for whole-slide image retrieval in cancer pathology data. The research found that while the TITAN foundation model performed best, its advantage over patc…
-
Foundation models aid lung cancer growth pattern prediction with attention-based learning
Researchers have developed an attention-based multiple instance learning (ABMIL) framework to predict lung adenocarcinoma growth patterns from whole slide images. This method reduces the need for extensive annotations b…