390 citations · 495 across the 12 of their papers we have counts for
12 papers
Radiology-Llama2: Best-in-Class Large Language Model for Radiology
Zhengliang Liu, Yiwei Li, Peng Shu +18
This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning. Radiology-Llama2 is based on the Llama2 arch…
Radiology-GPT: A Large Language Model for Radiology
Zhengliang Liu, Aoxiao Zhong, Yiwei Li +17
We introduce Radiology-GPT, a large language model for radiology. Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstr…
Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift
Wenao Ma, Cheng Chen, Shuang Zheng +3
Class distribution plays an important role in learning deep classifiers. When the proportion of each class in the test set differs from the training set, the performance of classif…
Online Reflective Learning for Robust Medical Image Segmentation
Yuhao Huang, Xin Yang, Xiaoqiong Huang +7
Deep segmentation models often face the failure risks when the testing image presents unseen distributions. Improving model robustness against these risks is crucial for the large-…
Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary
Quande Liu, Cheng Chen, Qi Dou +1
Domain generalization typically requires data from multiple source domains for model learning. However, such strong assumption may not always hold in practice, especially in medica…
3D-model ShapeNet Core Classification using Meta-Semantic Learning
Farid Ghareh Mohammadi, Cheng Chen, Farzan Shenavarmasouleh +3
Understanding 3D point cloud models for learning purposes has become an imperative challenge for real-world identification such as autonomous driving systems. A wide variety of sol…
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
Hongzheng Yang, Cheng Chen, Meirui Jiang +4
Test-time adaptation (TTA) has increasingly been an important topic to efficiently tackle the cross-domain distribution shift at test time for medical images from different institu…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation
Yueming Jin, Yang Yu, Cheng Chen +3
Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre. Previous works rely on conventional aggregation module…
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion
Cheng Chen, Qi Dou, Yueming Jin +3
Accurate medical image segmentation commonly requires effective learning of the complementary information from multimodal data. However, in clinical practice, we often encounter th…
Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
Cheng Chen, Qi Dou, Hao Chen +2
This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Dom…
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation
Cheng Chen, Qi Dou, Hao Chen +1
In spite of the compelling achievements that deep neural networks (DNNs) have made in medical image computing, these deep models often suffer from degraded performance when being a…