A5086021882- Retinal Imaging and Analysis
- Robotics and Sensor-Based Localization
- Intelligent Tutoring Systems and Adaptive Learning
- Retinal Diseases and Treatments
- Remote Sensing and LiDAR Applications
- Artificial Intelligence in Healthcare and Education
- Optical Coherence Tomography Applications
- Retinal and Optic Conditions
- COVID-19 diagnosis using AI
- Neural Networks and Applications
- Reinforcement Learning in Robotics
- Multimodal Machine Learning Applications
- 3D Surveying and Cultural Heritage
- Medical Image Segmentation Techniques
- Topic Modeling
Tsinghua–Berkeley Shenzhen Institute
2024
Tsinghua University
2024
Civil Aviation University of China
2021
The accurate segmentation and quantification of retinal fluid in Optical Coherence Tomography (OCT) images are crucial for the diagnosis treatment ophthalmic diseases such as age-related macular degeneration. However, is challenging due to significant variations size, position, shape fluid, well their complex, curved boundaries. To address these challenges, we propose a novel multi-scale feature fusion attention network (FNeXter), based on ConvNeXt Transformer, OCT segmentation. In FNeXter,...
Large multimodal language models (LMMs) have achieved significant success in general domains. However, due to the differences between medical images and text web content, performance of LMMs scenarios is limited. In ophthalmology, clinical diagnosis relies on multiple modalities images, but unfortunately, ophthalmic large not been explored date. this paper, we study construct an model. Firstly, use fundus as entry point build a disease assessment pipeline achieve common lesion segmentation....
Tabular data is the most common type of in real-life scenarios. In this study, we propose TabKANet model for tabular modeling, which targets bottlenecks learning from numerical content. We constructed a Kolmogorov-Arnold Network (KAN) based Numerical Embedding Module and unified categorical features encoding within Transformer architecture. has demonstrated stable significantly superior performance compared to Neural Networks (NNs) across multiple public datasets binary classification,...