14 citations · 34 across the 9 of their papers we have counts for
9 papers
NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning
Yanyi Su, Hongshuai Wang, Zhifeng Gao +1
Olfaction lies at the intersection of chemical structure, neural encoding, and linguistic perception, yet existing representation methods fail to fully capture this pathway. Curren…
MedP-CLIP: Medical CLIP with Region-Aware Prompt Integration
Jiahui Peng, He Yao, Jingwen Li +9
Contrastive Language-Image Pre-training (CLIP) has demonstrated outstanding performance in global image understanding and zero-shot transfer through large-scale text-image alignmen…
SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation
Yujie Lu, Jingwen Li, Sibo Ju +5
Medical image segmentation is vital for clinical diagnosis and quantitative analysis, yet remains challenging due to the heterogeneity of imaging modalities and the high cost of pi…
LG-CD: Enhancing Language-Guided Change Detection through SAM2 Adaptation
Yixiao Liu, Yizhou Yang, Jinwen Li +5
Remote Sensing Change Detection (RSCD) typically identifies changes in land cover or surface conditions by analyzing multi-temporal images. Currently, most deep learning-based meth…
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
Junlong Cheng, Bin Fu, Jin Ye +10
Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model gen…
SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
Guoan Wang, Jin Ye, Junlong Cheng +5
Volumetric medical image segmentation is pivotal in enhancing disease diagnosis, treatment planning, and advancing medical research. While existing volumetric foundation models for…
SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks
Jin Ye, Junlong Cheng, Jianpin Chen +12
Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes. Its success largely owes to massiv…
SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
Haoyu Wang, Sizheng Guo, Jin Ye +11
Existing volumetric medical image segmentation models are typically task-specific, excelling at specific target but struggling to generalize across anatomical structures or modalit…
A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation
Ziyan Huang, Zhongying Deng, Jin Ye +11
Although deep learning have revolutionized abdominal multi-organ segmentation, models often struggle with generalization due to training on small, specific datasets. With the recen…