jlcsilva/EfficientUNetPlusPlus
Decoder architecture based on the UNet++. Combining residual bottlenecks with depthwise convolutions and attention mechanisms, it outperforms the UNet++ in a coronary artery segmentation task, while being significantly more computationally efficient.
This project offers an improved way to automatically identify and outline specific structures within medical images, such as coronary arteries or retinal vessels. It takes raw medical image scans as input and outputs precise segmented images, which can aid in diagnosis or treatment planning. Medical imaging specialists, researchers, or clinicians working with diagnostic image analysis would find this beneficial.
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Use this if you need to perform highly accurate and computationally efficient segmentation of anatomical structures in medical images, especially in cardiology or ophthalmology.
Not ideal if you are looking for a general-purpose image analysis tool not focused on medical image segmentation, or if your primary concern is interpretability over raw performance and efficiency.
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67
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Language
Python
License
MIT
Category
Last pushed
Jan 20, 2022
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