1.2k citations · 1.6k across the 39 of their papers we have counts for
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HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving
Xinpeng Ding, Jianhua Han, Hang Xu +2
Recent efforts to use natural language for interpretable driving focus mainly on planning, neglecting perception tasks. In this paper, we address this gap by introducing ROLISP (Ri…
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No
Hualiang Wang, Yi Li, Huifeng Yao +1
Out-of-distribution (OOD) detection refers to training the model on an in-distribution (ID) dataset to classify whether the input images come from unknown classes. Considerable eff…
Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes
Weihang Dai, Xiaomeng Li, Taihui Yu +3
Atrial Fibrillation (AF) is characterized by rapid, irregular heartbeats, and can lead to fatal complications such as heart failure. The disease is divided into two sub-types based…
Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images
Lehan Wang, Weihang Dai, Mei Jin +2
Optical Coherence Tomography (OCT) is a novel and effective screening tool for ophthalmic examination. Since collecting OCT images is relatively more expensive than fundus photogra…
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification
Marawan Elbatel, Hualiang Wang, Robert Martí +2
In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly…
Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping
Qixiang Zhang, Yi Li, Cheng Xue +1
Designing deep learning algorithms for gland segmentation is crucial for automatic cancer diagnosis and prognosis, yet the expensive annotation cost hinders the development and app…
DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation
Haonan Wang, Xiaomeng Li
The volume-wise labeling of 3D medical images is expertise-demanded and time-consuming; hence semi-supervised learning (SSL) is highly desirable for training with limited labeled d…
Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution
Wenxuan Chen, Sirui Wu, Shuai Wang +4
Multi-contrast magnetic resonance imaging (MRI) reflects information about human tissue from different perspectives and has many clinical applications. By utilizing the complementa…
FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition
Marawan Elbatel, Robert Martí, Xiaomeng Li
Representational transfer from publicly available models is a promising technique for improving medical image classification, especially in long-tailed datasets with rare diseases.…
Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation
Xinyue Xu, Yuhan Hsi, Haonan Wang +1
Medical image data are often limited due to the expensive acquisition and annotation process. Hence, training a deep-learning model with only raw data can easily lead to overfittin…
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation
Huimin Wu, Xiaomeng Li, Yiqun Lin +1
This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the exist…
A Closer Look at the Explainability of Contrastive Language-Image Pre-training
Yi Li, Hualiang Wang, Yiqun Duan +2
Contrastive language-image pre-training (CLIP) is a powerful vision-language model that has shown great benefits for various tasks. However, we have identified some issues with its…
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval
Xunguang Wang, Jiawang Bai, Xinyue Xu +1
Deep hashing has been extensively applied to massive image retrieval due to its efficiency and effectiveness. Recently, several adversarial attacks have been presented to reveal th…
Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction
Yiqun Lin, Zhongjin Luo, Wei Zhao +1
Sparse-view cone-beam CT (CBCT) reconstruction is an important direction to reduce radiation dose and benefit clinical applications. Previous voxel-based generation methods represe…
Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks
Weihang Dai, Xiaomeng Li, Kwang-Ting Cheng
Deep regression is an important problem with numerous applications. These range from computer vision tasks such as age estimation from photographs, to medical tasks such as ejectio…