2024
DOI: 10.1109/tmi.2023.3312524
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Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data

Huy Hoang Nguyen,
Matthew B. Blaschko,
Simo Saarakkala
et al.

Abstract: Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face is how to predict the future trajectory of a disease. Current methods for prognosis or disease trajectory forecasting often require domain knowledge and are complicated to apply. In this paper, we formulate the prognosis prediction problem as a one-to-many prediction problem. Inspired by a clinical decision-making process with two ag… Show more

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Cited by 7 publications
(1 citation statement)
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“…It should be noted, however, that some transformer-based models are increasingly used to perform multimodal tasks in the medical field [60][61][62]. It has been observed that these models often combine a CNN structure with a transformer structure, resulting in excellent classification performance with limited medical datasets; this is one of the directions that we plan to pursue in the future.…”
Section: Discussionmentioning
confidence: 99%
“…It should be noted, however, that some transformer-based models are increasingly used to perform multimodal tasks in the medical field [60][61][62]. It has been observed that these models often combine a CNN structure with a transformer structure, resulting in excellent classification performance with limited medical datasets; this is one of the directions that we plan to pursue in the future.…”
Section: Discussionmentioning
confidence: 99%