Patient-level thyroid cancer classification using attention multiple instance learning on fused multi-scale ultrasound image features.
AMIA Annu Symp Proc
; 2023: 1344-1353, 2023.
Article
in En
| MEDLINE
| ID: mdl-38222341
ABSTRACT
For patients with thyroid nodules, the ability to detect and diagnose a malignant nodule is the key to creating an appropriate treatment plan. However, assessments of ultrasound images do not accurately represent malignancy, and often require a biopsy to confirm the diagnosis. Deep learning techniques can classify thyroid nodules from ultrasound images, but current methods depend on manually annotated nodule segmentations. Furthermore, the heterogeneity in the level of magnification across ultrasound images presents a significant obstacle to existing methods. We developed a multi-scale, attention-based multiple-instance learning model which fuses both global and local features of different ultrasound frames to achieve patient-level malignancy classification. Our model demonstrates improved performance with an AUROC of 0.785 (p<0.05) and AUPRC of 0.539, significantly surpassing the baseline model trained on clinical features with an AUROC of 0.667 and AUPRC of 0.444. Improved classification performance better triages the need for biopsy.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Thyroid Neoplasms
/
Thyroid Nodule
Type of study:
Diagnostic_studies
Limits:
Humans
Language:
En
Journal:
AMIA Annu Symp Proc
Journal subject:
INFORMATICA MEDICA
Year:
2023
Document type:
Article
Affiliation country:
Estados Unidos