bibtext: @InProceedings{ Zha_DSCENet_MICCAI2024,
                   author = { Zhang, Yuan and Qi, Yaolei and Qi, Xiaoming and Wei, Yongyue and Yang, Guanyu },
                   title = { { DSCENet: Dynamic Screening and Clinical-Enhanced Multimodal Fusion for MPNs Subtype Classification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
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.
title: DSCENet: Dynamic Screening and Clinical-Enhanced Multimodal Fusion for MPNs Subtype Classification
authors: Zhang, Yuan Qi, Yaolei Qi, Xiaoming Wei, Yongyue Yang, Guanyu