18 research outputs found
ACHIKO-M Database for high myopia analysis and its evaluation
Myopia is the leading public health concern with high prevalence in developed countries. In this paper, we present the ACHIKO-M fundus image database with both myopic and emmetropic cases for high myopia study. The database contains 705 myopic subjects and 151 normal subjects with both left eye and right eye images for each subject. In addition, various clinical data is also available, allowing correlation study of different risk factors. We evaluated two state-of-the-art automated myopia detection algorithms on this database to show how it can be used. Both methods achieve more than 90% accuracy for myopia diagnosis. We will also discuss how ACHIKO-M can be a good database for both scientific and clinical research of myopia
Multiple ocular diseases detection by graph regularized multi-label learning
We develop a general framework for multiple ocular diseases diagnosis, based on Graph Regularized Multi-label Learning (GRML). Glaucoma, Pathological Myopia (PM), and Age-related Macular Degeneration (AMD) are three leading ocular diseases in the world. By exploiting the correlations among these three diseases, a novel GRML scheme is investigated for a simultaneous detection of these three leading ocular diseases for a given fundus image. We validate our GRML framework by conducting extensive experiments on SiMES dataset. The results show area under curve (AUC) of the receiver operating characteristic curve in multiple ocular diseases detection are much better than traditional popular algorithms. The method could be used for glaucoma, PM, and AMD diagnosis
Background suppressing Gabor energy filtering
In the field of facial emotion recognition, early research advanced with the use of Gabor filters. However, these filters lack generalization and result in undesirably large feature vector size. In recent work, more attention has been given to other local appearance features. Two desired characteristics in a facial appearance feature are generalization capability, and the compactness of representation. In this paper, we propose a novel texture feature inspired by Gabor energy filters, called background suppressing Gabor energy filtering. The feature has a generalization component that removes background texture. It has a reduced feature vector size due to maximal representation and soft orientation histograms, and it is awhite box representation. We demonstrate improved performance on the non-trivial Audio/Visual Emotion Challenge 2012 grand-challenge dataset by a factor of 7.17 over the Gabor filter on the development set. We also demonstrate applicability of our approach beyond facial emotion recognition which yields improved classification rate over the Gabor filter for four bioimaging datasets by an average of 8.22%
Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions
Deep learning has seen rapid growth in recent years and achieved
state-of-the-art performance in a wide range of applications. However, training
models typically requires expensive and time-consuming collection of large
quantities of labeled data. This is particularly true within the scope of
medical imaging analysis (MIA), where data are limited and labels are expensive
to be acquired. Thus, label-efficient deep learning methods are developed to
make comprehensive use of the labeled data as well as the abundance of
unlabeled and weak-labeled data. In this survey, we extensively investigated
over 300 recent papers to provide a comprehensive overview of recent progress
on label-efficient learning strategies in MIA. We first present the background
of label-efficient learning and categorize the approaches into different
schemes. Next, we examine the current state-of-the-art methods in detail
through each scheme. Specifically, we provide an in-depth investigation,
covering not only canonical semi-supervised, self-supervised, and
multi-instance learning schemes, but also recently emerged active and
annotation-efficient learning strategies. Moreover, as a comprehensive
contribution to the field, this survey not only elucidates the commonalities
and unique features of the surveyed methods but also presents a detailed
analysis of the current challenges in the field and suggests potential avenues
for future research.Comment: Update Few-shot Method
Frameworks in medical image analysis with deep neural networks
In recent years, deep neural network based medical image analysis has become quite powerful and achieved similar results performance-wise as experts. Consequently, the integration of these tools into the clinical routine as clinical decision support systems is highly desired. The benefits of automatic image analysis for clinicians are massive, ranging from improved diagnostic as well as treatment quality to increased time-efficiency through automated structured reporting. However, implementations in the literature revealed a significant lack of standardization in pipeline building resulting in low reproducibility, high complexity through extensive knowledge requirements for building state-of-the-art pipelines, and difficulties for application in clinical research.
The main objective of this work is the standardization of pipeline building in deep neural network based medical image segmentation and classification. This is why the Python frameworks MIScnn for medical image segmentation and AUCMEDI for medical image classification are proposed which simplify the implementation process through intuitive building blocks eliminating the need for time-consuming and error-prone implementation of common components from scratch. The proposed frameworks include state-of-the-art methodology, follow outstanding open-source principles like extensive documentation as well as stability, offer rapid as well as simple application capabilities for deep learning experts as well as clinical researchers, and provide cutting-edge high-performance competitive with the strongest implementations in the literature.
As secondary objectives, this work presents more than a dozen in-house studies as well as discusses various external studies utilizing the proposed frameworks in order to prove the capabilities of standardized medical image analysis. The presented studies demonstrate excellent predictive capabilities in applications ranging from COVID-19 detection in computed tomography scans to the integration into a clinical study workflow for Gleason grading of prostate cancer microscopy sections and advance the state-of-the-art in medical image analysis by simplifying experimentation setups for research. Furthermore, studies for increasing reproducibility in performance assessment of medical image segmentation are presented including an open-source metric library for standardized evaluation and a community guideline on proper metric usage.
The proposed contributions in this work improve the knowledge representation of the field, enable rapid as well as high-performing applications, facilitate further research, and strengthen the reproducibility of future studies
Learn to recognize pathological myopia in fundus images using bag-of-feature and sparse learning approach
10.1109/ISBI.2013.6556618Proceedings - International Symposium on Biomedical Imaging888-89