bibtext: @InProceedings{ Cob_Improved_MICCAI2024,
                   author = { Cobb, Robert and Cook, Gary J. R. and Reader, Andrew J. },
                   title = { { 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 } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: 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
authors: Cobb, Robert Cook, Gary J. R. Reader, Andrew J.