52 citations · 158 across the 24 of their papers we have counts for
7 papers · 1 filter
Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies
Pengcheng Chen, Ziyan Huang, Zhongying Deng +6
OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued considerable interest for its potential in medical applications. Despite its promise, recent studies a…
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…
SAM-Med2D
Junlong Cheng, Jin Ye, Zhongying Deng +12
The Segment Anything Model (SAM) represents a state-of-the-art research advancement in natural image segmentation, achieving impressive results with input prompts such as points an…
Focusing on Relevant Responses for Multi-modal Rumor Detection
Jun Li, Yi Bin, Liang Peng +4
In the absence of an authoritative statement about a rumor, people may expose the truth behind such rumor through their responses on social media. Most rumor detection methods aggr…
STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training
Ziyan Huang, Haoyu Wang, Zhongying Deng +8
Large-scale models pre-trained on large-scale datasets have profoundly advanced the development of deep learning. However, the state-of-the-art models for medical image segmentatio…