Mammographic Lesion Segmentation with Lightweight Models: A Comparative Study

• URL: http://arxiv.org/abs/2604.23899v1
• Date: Sun, 26 Apr 2026 22:01:35 GMT
• Title: Mammographic Lesion Segmentation with Lightweight Models: A Comparative Study
• Abstract summary: This study evaluates the performance and efficiency of lightweight models for mammographic lesion segmentation.<n>MobileNetV2 with Squeeze-and-Excitation (SCSE) achieved the best performance, with a Dice score of 0.5766.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast cancer is a leading cause of cancer-related mortality among women worldwide, with mammography as the primary screening tool. While deep learning models have shown strong performance in lesion segmentation, most rely on computationally intensive architectures that limit their use in resource-constrained environments. This study evaluates the performance and efficiency of lightweight models for mammographic lesion segmentation. Architectures including MobileNetV2, EfficientNet Lite, ENet, and Fast-SCNN were compared against a U-Net baseline using the INbreast dataset with 5-fold cross-validation. Performance was assessed using Dice score, Intersection over Union (IoU), and Recall, alongside model complexity. MobileNetV2 with Squeeze-and-Excitation (SCSE) achieved the best performance, with a Dice score of 0.5766 while using approximately 75\% fewer parameters than U-Net. Cross-dataset evaluation on the DMID dataset showed reduced accuracy due to domain shift but preserved recall. These results demonstrate that lightweight architectures offer a practical balance between performance and efficiency for deployable CAD systems.

Related papers

• Energy and Performance Benchmarking of Deep Learning Models for Breast Cancer Detection [0.0]
  We present a comparative analysis of seven deep learning models for breast cancer detection on two medical datasets: Breast Ultrasound and BreakHis 400X.<n>The evaluated architectures range from Convolutional Neural Networks (CNNs) and transformers to hybrid models.<n>Our results highlight the importance of jointly considering performance, emissions, and dataset characteristics when selecting models for medical applications.
  arXiv  Detail & Related papers  (2026-08-07T13:01:36Z)
• Improving Automated Wound Assessment Using Joint Boundary Segmentation and Multi-Class Classification Models [1.1432909951914676]
  This study presents a deep learning model based on YOLOv11 that simultaneously performs wound boundary segmentation (WBS) and wound classification (WC) across five clinically relevant wound types.<n>The results confirm the model's robustness against complex backgrounds and high intra-class variability.
  arXiv  Detail & Related papers  (2026-03-28T16:11:31Z)
• Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression [53.39128997308138]
  We introduce information capacity, a measure of model efficiency based on text compression performance.<n> Empirical evaluations on mainstream open-source models show that models of varying sizes within a series exhibit consistent information capacity.<n>A distinctive feature of information capacity is that it incorporates tokenizer efficiency, which affects both input and output token counts.
  arXiv  Detail & Related papers  (2025-11-11T10:07:32Z)
• Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging [40.35825564674249]
  This study introduces the first structured benchmark to assess the robustness and efficiency of transfer learning strategies for Foundation Models.<n>Four publicly available COVID-19 chest X-ray datasets were used, covering mortality, severity, and admission.<n>CNNs pretrained on ImageNet and FMs pretrained on general or biomedical datasets were adapted using full finetuning, linear probing, and parameter-efficient methods.
  arXiv  Detail & Related papers  (2025-06-23T09:16:04Z)
• The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: a preliminary study [37.69303106863453]
  Deep learning is advancing the analysis of electroencephalography (EEG) data by effectively discovering highly nonlinear patterns.<n>No comprehensive guidelines for proper data partitioning and cross-validation exist in the domain.<n>This paper thoroughly investigates the role of data partitioning and cross-validation in evaluating EEG deep learning models.
  arXiv  Detail & Related papers  (2025-05-19T12:05:28Z)
• Towards Faster and More Compact Foundation Models for Molecular Property Prediction [44.64301507940171]
  Joint Multi-domain Pre-training (JMP) foundation model has demonstrated strong performance across various downstream tasks.<n>Despite JMP's advantages, fine-tuning it on molecular datasets ranging from small-scale to large-scale requires considerable time and computational resources.<n>Our study provides insights for developing lighter, faster, and more scalable foundation models for molecular and materials discovery.
  arXiv  Detail & Related papers  (2025-04-28T07:41:03Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan [5.5193366921929155]
  We evaluate and compare the performance of three CNN-based models, i.e., U-Net, Attention U-Net, and U-KAN, and one transformer-based model, SAM 2.<n>The dataset comprises 11,164 MRI scans with detailed annotations of femoral regions.<n> Attention U-Net achieves the highest overall scores, while U-KAN demonstrated superior performance in anatomical regions with a smaller region of interest.
  arXiv  Detail & Related papers  (2025-04-05T05:47:56Z)
• Efficient Brain Tumor Classification with Lightweight CNN Architecture: A Novel Approach [0.0]
  Brain tumor classification using MRI images is critical in medical diagnostics, where early and accurate detection significantly impacts patient outcomes.<n>Recent advancements in deep learning (DL) have shown promise, but many models struggle with balancing accuracy and computational efficiency.<n>We propose a novel model architecture integrating separable convolutions and squeeze and excitation (SE) blocks, designed to enhance feature extraction while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-02-01T21:06:42Z)
• A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation [45.39707664801522]
  The retroperitoneum hosts a variety of tumors, including rare benign and malignant types, which pose diagnostic and treatment challenges.<n> Estimating tumor volume is difficult due to their irregular shapes, and manual segmentation is time-consuming.<n>This study evaluates U-Net enhancements, including CNN, ViT, Mamba, and xLSTM, on a new in-house CT dataset and a public organ segmentation dataset.
  arXiv  Detail & Related papers  (2025-02-01T04:25:28Z)
• Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks [37.41604955004456]
  Graph neural networks (GNNs) have demonstrated remarkable success in graph representation learning. Various sampling approaches have been proposed to scale GNNs to applications with large-scale graphs.
  arXiv  Detail & Related papers  (2024-10-07T18:29:02Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• LHU-Net: a Lean Hybrid U-Net for Cost-efficient, High-performance Volumetric Segmentation [4.168081528698768]
  We propose LHU-Net, a Lean Hybrid U-Net for volumetric medical image segmentation.<n>LHU-Net prioritizes spatial feature extraction before refining channel features, optimizing both efficiency and accuracy.<n> evaluated on four benchmark datasets (Synapse, Left Atrial, BraTS-Decathlon, and Lung-Decathlon)
  arXiv  Detail & Related papers  (2024-04-07T22:58:18Z)
• Dual-Activated Lightweight Attention ResNet50 for Automatic Histopathology Breast Cancer Image Classification [0.0]
  This study introduces a novel method for breast cancer classification, the Dual-Activated Lightweight Attention ResNet50 model. It integrates a pre-trained ResNet50 model with a lightweight attention mechanism, embedding an attention module in the fourth layer of ResNet50. The DALAResNet50 method was tested on breast cancer histopathology images from the BreakHis Database across magnification factors of 40X, 100X, 200X, and 400X, achieving accuracies of 98.5%, 98.7%, 97.9%, and 94.3%, respectively.
  arXiv  Detail & Related papers  (2023-08-25T03:08:41Z)
• ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance [61.04246102067351]
  We propose a foreground harmonization framework (ARHNet) to tackle intensity disparities and make synthetic images look more realistic. We demonstrate the efficacy of our method in improving the segmentation performance using real and synthetic images.
  arXiv  Detail & Related papers  (2023-07-02T10:39:29Z)
• Semi-Supervised Segmentation of Multi-vendor and Multi-center Cardiac MRI using Histogram Matching [0.0]
  We propose a semi-supervised segmentation setup for leveraging unlabeled data to segment Left-ventricle, Right-ventricle, and Myocardium. Handling the class imbalanced data issue using dice loss, the enhanced supervised model is able to achieve better dice scores. The model achieves average dice scores of 0.921, 0.926, and 0.891 for Left-ventricle, Right-ventricle, and Myocardium respectively.
  arXiv  Detail & Related papers  (2023-02-22T08:23:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.