  The precise subtype classification of myeloproliferative neoplasms (MPNs)
based on multimodal information, which assists clinicians in diagnosis and
long-term treatment plans, is of great clinical significance. However, it
remains a great challenging task due to the lack of diagnostic
representativeness for local patches and the absence of diagnostic-relevant
features from a single modality. In this paper, we propose a Dynamic Screening
and Clinical-Enhanced Network (DSCENet) for the subtype classification of MPNs
on the multimodal fusion of whole slide images (WSIs) and clinical information.
(1) A dynamic screening module is proposed to flexibly adapt the feature
learning of local patches, reducing the interference of irrelevant features and
enhancing their diagnostic representativeness. (2) A clinical-enhanced fusion
module is proposed to integrate clinical indicators to explore complementary
features across modalities, providing comprehensive diagnostic information. Our
approach has been validated on the real clinical data, achieving an increase of
7.91% AUC and 16.89% accuracy compared with the previous state-of-the-art
(SOTA) methods. The code is available at https://github.com/yuanzhang7/DSCENet.
