Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication.

ABSTRACT:

Background

Deep learning models can infer cancer patient prognosis from molecular and anatomic pathology information. Recent studies that leveraged information from complementary multimodal data improved prognostication, further illustrating the potential utility of such methods. However, current approaches: 1) do not comprehensively leverage biological and histomorphological relationships and 2) make use of emerging strategies to "pretrain" models (i.e., train models on a slightly orthogonal dataset/modeling objective) which may aid prognostication by reducing the amount of information required for achieving optimal performance. In addition, model interpretation is crucial for facilitating the clinical adoption of deep learning methods by fostering practitioner understanding and t

SUBMITTER: Azher ZL 

PROVIDER: S-EPMC10363299 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature