TY - CHAP
T1 - Improved Classification Learning from Highly Imbalanced Multi-label Datasets of Inflamed Joints in [99mTc]Maraciclatide Imaging of Arthritic Patients by Natural Image and Diffusion Model Augmentation
AU - Cobb, Robert
AU - Cook, Gary
AU - Reader, Andrew
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/10/4
Y1 - 2024/10/4
N2 - Gamma camera imaging of the novel radiopharmaceutical [99mTc]maraciclatide can be used to detect inflammation in patients with rheumatoid arthritis. Due to the novelty of this clinical imaging application, data are especially scarce with only one dataset composed of 48 patients available for development of classification models. In this work we classify inflammation in individual joints in the hands of patients using only this small dataset. Our methodology combines diffusion models to augment the available training data for this classification task from an otherwise small and imbalanced dataset. We also explore the use of augmenting with a publicly available natural image dataset in combination with a diffusion model. We use a DenseNet model to classify the inflammation of individual joints in the hand. Our results show that compared to non-augmented baseline classification accuracy, sensitivity, and specificity metrics of 0.79 ± 0.05, 0.50 ± 0.04, and 0.85 ± 0.05, respectively our method improves model performance for these metrics to 0.91 ± 0.02, 0.79 ± 0.11, 0.93 ± 0.02, respectively. When we use an ensemble model and combine natural image augmentation with [99mTc]maraciclatide augmentation we see performance increase to 0.92 ± 0.02, 0.80 ± 0.09, 0.95 ± 0.02 for accuracy, sensitivity, and specificity, respectively.
AB - Gamma camera imaging of the novel radiopharmaceutical [99mTc]maraciclatide can be used to detect inflammation in patients with rheumatoid arthritis. Due to the novelty of this clinical imaging application, data are especially scarce with only one dataset composed of 48 patients available for development of classification models. In this work we classify inflammation in individual joints in the hands of patients using only this small dataset. Our methodology combines diffusion models to augment the available training data for this classification task from an otherwise small and imbalanced dataset. We also explore the use of augmenting with a publicly available natural image dataset in combination with a diffusion model. We use a DenseNet model to classify the inflammation of individual joints in the hand. Our results show that compared to non-augmented baseline classification accuracy, sensitivity, and specificity metrics of 0.79 ± 0.05, 0.50 ± 0.04, and 0.85 ± 0.05, respectively our method improves model performance for these metrics to 0.91 ± 0.02, 0.79 ± 0.11, 0.93 ± 0.02, respectively. When we use an ensemble model and combine natural image augmentation with [99mTc]maraciclatide augmentation we see performance increase to 0.92 ± 0.02, 0.80 ± 0.09, 0.95 ± 0.02 for accuracy, sensitivity, and specificity, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85206585420&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-72086-4_32
DO - 10.1007/978-3-031-72086-4_32
M3 - Conference paper
SN - 9783031720857
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 339
EP - 348
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings
A2 - Linguraru, Marius George
A2 - Dou, Qi
A2 - Feragen, Aasa
A2 - Giannarou, Stamatia
A2 - Glocker, Ben
A2 - Lekadir, Karim
A2 - Schnabel, Julia A.
ER -