Factors determining generalization in deep learning models for scoring COVID-CT images

Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives

CLINICAL RELEVANCE

Glaucoma is a complex eye condition with varied morphological and clinical presentations, making diagnosis and management challenging. The lack of a consensus definition for glaucoma or glaucomatous optic neuropathy further complicates the development of universal diagnostic tools. Developing robust artificial intelligence (AI) models for glaucoma screening is essential for early detection and treatment but faces significant obstacles. Effective deep learning algorithms require large, well-curated datasets from diverse patient populations and imaging protocols. However, creating centralized data repositories is hindered by concerns over data sharing, patient privacy, regulatory compliance, and intellectual property. Federated Learning (FL) offers a potential solution by enabling data to remain locally hosted while facilitating distributed model training across multiple sites.

METHODS

A comprehensive literature review was conducted on the application of Federated Learning in training AI models for glaucoma screening. Publications from 1950 to 2024 were searched using databases such as PubMed and IEEE Xplore with keywords including "glaucoma," "federated learning," "artificial intelligence," "deep learning," "machine learning," "distributed learning," "privacy-preserving," "data sharing," "medical imaging," and "ophthalmology." Articles were included if they discussed the use of FL in glaucoma-related AI tasks or addressed data sharing and privacy challenges in ophthalmic AI development.

RESULTS

FL enables collaborative model development without centralizing sensitive patient data, addressing privacy and regulatory concerns. Studies show that FL can improve model performance and generalizability by leveraging diverse datasets while maintaining data security. FL models have achieved comparable or superior accuracy to those trained on centralized data, demonstrating effectiveness in real-world clinical settings.

CONCLUSIONS

Federated Learning presents a promising strategy to overcome current obstacles in developing AI models for glaucoma screening. By balancing the need for extensive, diverse training data with the imperative to protect patient privacy and comply with regulations, FL facilitates collaborative model training without compromising data security. This approach offers a pathway toward more accurate and generalizable AI solutions for glaucoma detection and management.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references in the Footnotes and Disclosures at the end of this article.

Group theoretic particle swarm optimization for gray-level medical image enhancement

As a principal category in the promising field of medical image processing, medical image enhancement has a powerful influence on the intermedia features and final results of the computer aided diagnosis (CAD) system by increasing the capacity to transfer the image information in the optimal form. The enhanced region of interest (ROI) would contribute to the early diagnosis and the survival rate of patients. Meanwhile, the enhancement schema can be treated as the optimization approach of image grayscale values, and metaheuristics are adopted popularly as the mainstream technologies for medical image enhancement. In this study, we propose an innovative metaheuristic algorithm named group theoretic particle swarm optimization (GT-PSO) to tackle the optimization problem of image enhancement. Based on the mathematical foundation of symmetric group theory, GT-PSO comprises particle encoding, solution landscape, neighborhood movement and swarm topology. The corresponding search paradigm takes place simultaneously under the guidance of hierarchical operations and random components, and it could optimize the hybrid fitness function of multiple measurements of medical images and improve the contrast of intensity distribution. The numerical results generated from the comparative experiments show that the proposed GT-PSO has outperformed most other methods on the real-world dataset. The implication also indicates that it would balance both global and local intensity transformations during the enhancement process.

COVID-19 disease identification network based on weakly supervised feature selection

The coronavirus disease 2019 (COVID-19) outbreak has resulted in countless infections and deaths worldwide, posing increasing challenges for the health care system. The use of artificial intelligence to assist in diagnosis not only had a high accuracy rate but also saved time and effort in the sudden outbreak phase with the lack of doctors and medical equipment. This study aimed to propose a weakly supervised COVID-19 classification network (W-COVNet). This network was divided into three main modules: weakly supervised feature selection module (W-FS), deep learning bilinear feature fusion module (DBFF) and Grad-CAM++ based network visualization module (Grad-Ⅴ). The first module, W-FS, mainly removed redundant background features from computed tomography (CT) images, performed feature selection and retained core feature regions. The second module, DBFF, mainly used two symmetric networks to extract different features and thus obtain rich complementary features. The third module, Grad-Ⅴ, allowed the visualization of lesions in unlabeled images. A fivefold cross-validation experiment showed an average classification accuracy of 85.3%, and a comparison with seven advanced classification models showed that our proposed network had a better performance.

Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection

BACKGROUND

Artificial intelligence technologies in classification/detection of COVID-19 positive cases suffer from generalizability. Moreover, accessing and preparing another large dataset is not always feasible and time-consuming. Several studies have combined smaller COVID-19 CT datasets into "supersets" to maximize the number of training samples. This study aims to assess generalizability by splitting datasets into different portions based on 3D CT images using deep learning.

METHOD

Two large datasets, including 1110 3D CT images, were split into five segments of 20% each. Each dataset's first 20% segment was separated as a holdout test set. 3D-CNN training was performed with the remaining 80% from each dataset. Two small external datasets were also used to independently evaluate the trained models.

RESULTS

The total combination of 80% of each dataset has an accuracy of 91% on Iranmehr and 83% on Moscow holdout test datasets. Results indicated that 80% of the primary datasets are adequate for fully training a model. The additional fine-tuning using 40% of a secondary dataset helps the model generalize to a third, unseen dataset. The highest accuracy achieved through transfer learning was 85% on LDCT dataset and 83% on Iranmehr holdout test sets when retrained on 80% of Iranmehr dataset.

CONCLUSION

While the total combination of both datasets produced the best results, different combinations and transfer learning still produced generalizable results. Adopting the proposed methodology may help to obtain satisfactory results in the case of limited external datasets.