Lightweight AI models show promise for efficient mammographic lesion segmentation

A new study published on arXiv evaluates the effectiveness of lightweight deep learning models for segmenting lesions in mammograms. Researchers compared architectures like MobileNetV2 and EfficientNet Lite against a U-Net baseline, using metrics such as Dice score and Intersection over Union. The MobileNetV2 model with SCSE demonstrated superior performance with significantly fewer parameters than U-Net, suggesting its potential for practical computer-aided detection systems in resource-limited settings. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Lightweight models offer a viable path for deploying mammographic lesion segmentation in resource-constrained environments.

RANK_REASON Academic paper evaluating existing models on a specific task.

Read on arXiv cs.CV →

• arXiv
• MobileNetV2
• EfficientNet Lite
• ENet
• Fast-SCNN
• U-Net
• INbreast dataset
• DMID dataset

• paper
• other