arXiv Machine Learning By Bo Cao, Fan Yu, Mengmeng Feng, SenHao Zhang, Xin Meng, Yue Zhang, Zhen Qian, Jie Lu

Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI

Read the original on arXiv Machine Learning →

arXiv:2509. 11924v2 Announce Type: cross Abstract: Multimodal learning has attracted much attention in recent years due to its ability to effectively utilize data features from a variety of different modalities.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jul 17

Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging

arXiv:2607. 14995v1 Announce Type: new Abstract: Multimodal Contrastive Learning (CL) has shown significant performance in aligning representations across various data modalities and improving downstream tasks, especially in healthcare.

By Sara Ketabi, Matthias W. Wagner, Cynthia Hawkins, Uri Tabori, Birgit Betina Ertl-Wagner, Farzad Khalvati
arXiv Machine Learning
Jul 30

Challenges and proposed solutions in modeling multimodal medical data: A systematic review

arXiv:2505. 06945v5 Announce Type: replace Abstract: Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records.

By Maryam Farhadizadeh, Maria Weymann, Michael Bla{\ss}, Johann Kraus, Christopher Gundler, Sebastian Walter, Noah Hempen, Hannah Bast, Harald Binder, Nadine Binder