Uncertainty-aware and causal test-time adaptive foundation model for robust colorectal cancer pathology diagnosis

Nature·Dec 6

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A model for robust colorectal cancer pathology diagnosis has been developed, incorporating uncertainty awareness and causal test-time adaptability. The specifics of the model and its applications are being discussed in various technical tracks and publications, including those from the Association for the Advancement of Artificial Intelligence and Nature. The exact nature and scope of the model's capabilities and potential uses are not entirely clear from the available information.

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Vol. 40 No. 39: AAAI-26 Technical Tracks 39

The Association for the Advancement of Artificial Intelligence·Mar 17

A causal multimodal framework for privacy-preserving early-stage cancer detection and adaptive testing

Nature·Mar 11

Vol. 40 No. 24: AAAI-26 Technical Tracks 24

The Association for the Advancement of Artificial Intelligence·Mar 17

Proceedings of the AAAI Conference on Artificial Intelligence

The Association for the Advancement of Artificial Intelligence·Mar 17

Structure-aware fusion learning and intelligent decision support based on dynamic flight parameter hypergraphs in flight test

Nature·Apr 14

On the foundations of Earth foundation models - Communications Earth & Environment

Nature·Jan 8

Vol. 40 No. 47: AAAI-26 New Faculty Highlights, Journal Track, IAAI-26 and EAAI-26 Main Track

The Association for the Advancement of Artificial Intelligence·Mar 17

Unifying graph neural networks causal machine learning and conformal prediction for robust causal inference in rail transport systems

Nature·Nov 7

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Uncertainty-aware and causal test-time adaptive foundation model for robust colorectal cancer pathology diagnosis

Full coverage

Vol. 40 No. 39: AAAI-26 Technical Tracks 39

A causal multimodal framework for privacy-preserving early-stage cancer detection and adaptive testing

Vol. 40 No. 24: AAAI-26 Technical Tracks 24

Proceedings of the AAAI Conference on Artificial Intelligence

Structure-aware fusion learning and intelligent decision support based on dynamic flight parameter hypergraphs in flight test

On the foundations of Earth foundation models - Communications Earth & Environment

Vol. 40 No. 47: AAAI-26 New Faculty Highlights, Journal Track, IAAI-26 and EAAI-26 Main Track

Unifying graph neural networks causal machine learning and conformal prediction for robust causal inference in rail transport systems