A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset

• URL: http://arxiv.org/abs/2403.07105v1
• Date: Mon, 11 Mar 2024 18:57:45 GMT
• Title: A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset
• Authors: Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo H. O, Carlos F. Uribe, Rahul Dodhia, Juan L. Ferres, and Arman Rahmim
• Abstract summary: We train a ResNet-18 network to classify axial slices of lymphoma/CT images. Model performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) We observe and describe a performance overestimation in the case of slice-level split as compared to the patient-level split training.
• Score: 1.0318017891096118
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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