4 papers
DynCIM: Dynamic Curriculum for Imbalanced Multimodal Learning
Chengxuan Qian, Kai Han, Jiaxin Liu +6
Multimodal learning integrates complementary information from diverse modalities to enhance the decision-making process. However, the potential of multimodal collaboration remains…
Adaptive Label Correction for Robust Medical Image Segmentation with Noisy Labels
Chengxuan Qian, Kai Han, Jianxia Ding +4
Deep learning has shown remarkable success in medical image analysis, but its reliance on large volumes of high-quality labeled data limits its applicability. While noisy labeled d…
Frequency Domain Unlocks New Perspectives for Abdominal Medical Image Segmentation
Kai Han, Siqi Ma, Chengxuan Qian +4
Accurate segmentation of tumors and adjacent normal tissues in medical images is essential for surgical planning and tumor staging. Although foundation models generally perform wel…
CLIMD: A Curriculum Learning Framework for Imbalanced Multimodal Diagnosis
Kai Han, Chongwen Lyu, Lele Ma +5
Clinicians usually combine information from multiple sources to achieve the most accurate diagnosis, and this has sparked increasing interest in leveraging multimodal deep learning…