Xin Zhao

Xin Zhao
Airdoc · AI Research

About

22
Publications
2,978
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
67
Citations

Publications

Publications (22)
Article
Full-text available
Background Thyroid-associated ophthalmopathy (TAO) is one of the most common orbital diseases that seriously threatens visual function and significantly affects patients’ appearances, rendering them unable to work. This study established an intelligent diagnostic system for TAO based on facial images. Methods Patient images and data were obtained...
Preprint
Most of the medical tasks naturally exhibit a long-tailed distribution due to the complex patient-level conditions and the existence of rare diseases. Existing long-tailed learning methods usually treat each class equally to re-balance the long-tailed distribution. However, considering that some challenging classes may present diverse intra-class d...
Preprint
Full-text available
In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples), which results in a challenging imbalance learning scenario. For example, there are estimated more than 40 different kinds of retinal diseases with variable morbidity, however wi...
Chapter
Full-text available
In medical image segmentation, it is difficult to mark ambiguous areas accurately with binary masks, especially when dealing with small lesions. Therefore, it is a challenge for radiologists to reach a consensus by using binary masks under the condition of multiple annotations. However, these uncertain areas may contain anatomical structures that a...
Conference Paper
In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples), which results in a challenging imbalance learning scenario. For example, there are estimated more than 40 different kinds of retinal diseases with variable morbidity, however wi...
Preprint
Full-text available
Importance: The Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognized tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, effective, convenient and noninvasive tool for screening i...
Preprint
Full-text available
Retinal vessel segmentation plays a key role in computer-aided screening, diagnosis, and treatment of various cardiovascular and ophthalmic diseases. Recently, deep learning-based retinal vessel segmentation algorithms have achieved remarkable performance. However, due to the domain shift problem, the performance of these algorithms often degrades...
Article
Full-text available
Background Medical artificial intelligence (AI) has entered the clinical implementation phase, although real-world performance of deep-learning systems (DLSs) for screening fundus disease remains unsatisfactory. Our study aimed to train a clinically applicable DLS for fundus diseases using data derived from the real world, and externally test the m...
Preprint
Full-text available
In medical image segmentation, it is difficult to mark ambiguous areas accurately with binary masks, especially when dealing with small lesions. Therefore, it is a challenge for radiologists to reach a consensus by using binary masks under the condition of multiple annotations. However, these areas may contain anatomical structures that are conduci...
Preprint
Full-text available
In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples), which results in a challenging imbalance learning scenario. For example, there are estimated more than 40 different kinds of retinal diseases with variable morbidity, however wi...
Preprint
Full-text available
Background Ischemic cardiovascular diseases (ICVD) risk predict models are valuable but limited by its requirement for multidimensional medical information including that from blood drawing. A convenient and affordable alternative is in demand. Objectives To develop and validate a deep learning algorithm to predict 10-year ICVD risk using retinal f...
Preprint
Full-text available
Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognition even under a high noisy rate. In medical applications, learning from datasets with label noise is more challenging since medical imaging datasets tend to have asymmet...
Article
Deep-learning-based segmentation methods have shown great success across many medical image applications. However, the custom training paradigms suffer from a well-known constraint of the requirement of pixel-wise annotations, which is labor-intensive, especially when they are required to learn new classes incrementally. Contemporary incremental le...
Article
Full-text available
The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and only a limited amount of data is available for various retinal diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). Some studies show t...
Chapter
Recent application of deep learning in medical image achieves expert-level accuracy. However, the accuracy often degrades greatly on unseen data, for example data from different device designs and population distributions. In this work, we consider a realistic problem of domain generalization in fundus image analysis: when a model is trained on a c...
Preprint
Full-text available
Recently, ultra-widefield (UWF) 200°fundus imaging by Optos cameras has gradually been introduced because of its broader insights for detecting more information on the fundus than regular 30°-60°fundus cameras. Compared with UWF fundus images, regular fundus images contain a large amount of high-quality and well-annotated data. Due to the domain ga...
Preprint
Full-text available
The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and only a limited amount of data is available for various retinal diseases such as age-related macular degeneration (AMD) and diabetic retinopathy (DR). Some studies show t...
Chapter
The number of people suffering from retinal diseases increases with population aging and the popularity of electronic screens. Previous studies on deep learning based automatic screening generally focused on specific types of retinal diseases, such as diabetic retinopathy and glaucoma. Since patients may suffer from various types of retinal disease...
Conference Paper
Full-text available
The number of people suffering from retinal diseases increases with population aging and the popularity of electronic screens. Previous studies on deep learning based automatic screening generally focused on specific types of retinal diseases, such as diabetic retinopathy and glaucoma. Since patients may suffer from various types of retinal dis- ea...

Network

Cited By

Projects