Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Hierarchy-Aware and Knowledge-Guided Learning for Multi-Label Classification of Retinal Diseases From Fundus Images. |
| Συγγραφείς: |
Yang Z, Li Y, Liu Y |
| Πηγή: |
IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2026 Jun; Vol. 45 (6), pp. 3262-3275. |
| Τύπος έκδοσης: |
Journal Article |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 8310780 Publication Model: Print Cited Medium: Internet ISSN: 1558-254X (Electronic) Linking ISSN: 02780062 NLM ISO Abbreviation: IEEE Trans Med Imaging Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: New York, NY : Institute of Electrical and Electronics Engineers, c1982- |
| Ιατρικοί όροι (MeSH): |
Image Interpretation, Computer-Assisted*/methods , Retinal Diseases*/classification , Retinal Diseases*/diagnostic imaging , Classification Algorithms* , Diagnostic Techniques, Ophthalmological* , Fundus Oculi* , Machine Learning*, Retina/diagnostic imaging ; Humans |
| Περίληψη: |
Retinal diseases (RD) are major causes of global vision impairment. Automated diagnosis using fundus images has significant clinical value, particularly in multi-label classification of RD. Recently, hierarchy-aware methods have shown potential in improving classification performance by leveraging hierarchical relationships among disease categories. However, implicit hierarchy-aware methods often fail to capture complex semantic relationships required to make multi-level predictions, while explicit hierarchy-aware methods fail to maintain hierarchy level consistency. Additionally, existing approaches do not sufficiently integrate knowledge from expert domains. Accordingly, in this paper, we introduce a novel framework, namely Hierarchy-Aware and Knowledge-Guided Learning (HAKGL), for diagnosing RD from fundus images. It establishes complex relationships among diseases by employing a hierarchical Transformer for making multi-level predictions by maximally exploiting the visual information. Besides, we utilize feature similarities to establish correlations among hierarchy levels, which offer additional supervision signals to align hierarchical feature representations. This strategy explicitly maintains hierarchical consistency, thereby improving the performance of the model. Furthermore, we propose a correlation learning strategy for aligning image correlations with expert textual knowledge extracted from retinal foundation models, thus enabling the model to learn more generalizable representations. The superiority of the proposed HAKGL approach has been validated through extensive experiments in multi-label classification of RD. Code is available at https://github.com/YZC-99/HAKGL. |
| Entry Date(s): |
Date Created: 20260310 Date Completed: 20260607 Latest Revision: 20260610 |
| Update Code: |
20260610 |
| DOI: |
10.1109/TMI.2026.3672477 |
| PMID: |
41805508 |
| Βάση Δεδομένων: |
MEDLINE |