DSCENet: Dynamic Screening and Clinical-Enhanced Multimodal Fusion for MPNs Subtype Classification

TL;DR

DSCENet is a novel multimodal framework that improves MPN subtype classification by dynamically screening image features and integrating clinical data, leading to significant accuracy gains.

Contribution

The paper introduces a dynamic screening module and a clinical-enhanced fusion module for better multimodal feature integration in MPN classification.

Findings

Achieved 7.91% higher AUC over SOTA methods.

Improved classification accuracy by 16.89%.

Validated on real clinical data.

Abstract

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.