Towards robust multimodal ultrasound classification for liver tumor diagnosis: A generative approach to modality missingness.
Journal:
Computer methods and programs in biomedicine
PMID:
40188576
Abstract
BACKGROUND AND OBJECTIVE: In medical image analysis, combining multiple imaging modalities enhances diagnostic accuracy by providing complementary information. However, missing modalities are common in clinical settings, limiting the effectiveness of multimodal models. This study addresses the challenge of missing modalities in liver tumor diagnosis by proposing a generative model-based method for cross-modality reconstruction and classification. The dataset for this study comprises 359 case data from a hospital, with each case including three modality data: B-mode ultrasound images, Color Doppler Flow Imaging (CDFI), and clinical data. Only cases with one missing image modality are considered, excluding those with missing clinical data.