6 papers
LiMT: A Multi-task Liver Image Benchmark Dataset
Zhe Liu, Kai Han, Siqi Ma +10
Computer-aided diagnosis (CAD) technology can assist clinicians in evaluating liver lesions and intervening with treatment in time. Although CAD technology has advanced in recent y…
TriCon-Fair: Triplet Contrastive Learning for Mitigating Social Bias in Pre-trained Language Models
Chong Lyu, Lin Li, Shiqing Wu +1
The increasing utilization of large language models raises significant concerns about the propagation of social biases, which may result in harmful and unfair outcomes. However, ex…
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…