Alapfogalmak
UCM-Net is a novel, lightweight model that efficiently segments skin lesions using MLP and CNN, offering superior performance with minimal parameters.
Kivonat
The content introduces UCM-Net, a model for skin lesion segmentation. It addresses the challenges in accurately segmenting skin lesions from images for early diagnosis of skin cancer. The innovative UCM-Net Block combines MLP and CNN to reduce parameters while enhancing learning efficiency. Rigorous evaluations on datasets demonstrate its effectiveness with minimal computational requirements.
Abstract:
- Skin cancer poses a significant health challenge.
- Early diagnosis is crucial for effective treatment.
- Computer-aided systems aid in detecting and managing the disease.
Introduction:
- Skin cancer types include melanoma and non-melanoma.
- Importance of medical imaging in diagnosing skin cancer.
UCM-Net:
- Introduces a novel, efficient model for skin lesion segmentation.
- Combines MLP and CNN for robust feature learning.
Related Works:
- Overview of AI methods for biomedical image segmentation.
Network Design:
- UCM-Net's structural framework with encoder-decoder units.
Experiments and Results:
- Evaluation on PH2, ISIC 2017, and ISIC 2018 datasets.
Ablation results:
- Comparison of UCM-Net variants with U-Net models.
Conclusion:
- UCM-Net offers an efficient solution for skin lesion segmentation.
Statisztikák
UCM-NETは、50KB未満のパラメータで効果的なセグメンテーションを実現します。
UCM-NETは0.05 GLOPs未満の計算量で動作します。
Idézetek
"UCM-NETは、少ないパラメータと低いGFLOPsを維持しながら、優れた機能学習能力を提供します。" - Chunyu Yuan