A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset
<|reference_start|>A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset: Automated slice classification is clinically relevant since it can be incorporated into medical image segmentation workflows as a preprocessing step that would flag slices with a higher probability of containing tumors, thereby directing physicians attention to the important slices. In this work, we train a ResNet-18 network to classify axial slices of lymphoma PET/CT images (collected from two institutions) depending on whether the slice intercepted a tumor (positive slice) in the 3D image or if the slice did not (negative slice). Various instances of the network were trained on 2D axial datasets created in different ways: (i) slice-level split and (ii) patient-level split; inputs of different types were used: (i) only PET slices and (ii) concatenated PET and CT slices; and different training strategies were employed: (i) center-aware (CAW) and (ii) center-agnostic (CAG). Model performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC), and various binary classification metrics. We observe and describe a performance overestimation in the case of slice-level split as compared to the patient-level split training. The model trained using patient-level split data with the network input containing only PET slices in the CAG training regime was the best performing/generalizing model on a majority of metrics. Our models were additionally more closely compared using the sensitivity metric on the positive slices from their respective test sets.<|reference_end|>
@article{ahamed2024a,
    title={A slice classification neural network for automated classification of
  axial PET/CT slices from a multi-centric lymphoma dataset},
    author={Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo H. O, Carlos F. Uribe,
  Rahul Dodhia, Juan L. Ferres, and Arman Rahmim},
    journal={Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641Q
  (3 April 2023)},
    year={2024},
    doi={10.1117/12.2652947},
    archivePrefix={arXiv},
    eprint={2403.07105},
    primaryClass={eess.IV cs.CV cs.LG physics.med-ph}
}