LiFE-Net: Longitudinal information Fusion for Enhanced lesion detection in unsupervised learning contexts
Keywords: Longitudinal imaging, Liver lesion detection, Deep learning, CT scans
Abstract: Accurate detection of liver lesions in longitudinal follow-up is critical for assessing disease progression. Unlike clinical practices that compare multiple time points, most deep-learning approaches treat these time points independently. Existing longitudinal imaging methods, particularly in brain imaging, use strategies like channel-wise concatenation, recurrent architectures, or temporal difference computation. However, these methods might fall short in liver imaging due to challenges like non-rigid motions, anatomical variability, and changes in imaging conditions.
To address these challenges, we introduce LiFE-Net, the first framework to integrate longitudinal information from baseline liver CT scans through feature fusion. Our method employs intermediate feature fusion via self-attention mechanisms, leveraging baseline images to incorporate longitudinal information for more accurate predictions. We adopt an unsupervised training approach using synthetic lesions to address the lack of supervised datasets for longitudinal liver tumors.
Our results show improvements in detection performance on follow-up images when baseline information is incorporated, with gains in both detection mAP and ROC AUC per exam metrics.
An exhaustive ablation study further highlights the impact of baseline image integration, registration quality, and architectural components in achieving these improvements. Our code for LiFE-Net is made publicly available at: https://github.com/walid-yassine/LiFE-Net
Primary Subject Area: Detection and Diagnosis
Secondary Subject Area: Application: Radiology
Paper Type: Methodological Development
Registration Requirement: Yes
Reproducibility: https://github.com/walid-yassine/LiFE-Net
Visa & Travel: Yes
Submission Number: 166
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