FeijiangHan/CTISPED
UNet series network architectures (UNet, R2UNet, Attention UNet, Nested UNet, Tiny UNet etc.), combined with joint training of YOLO and other networks
This project helps medical professionals, specifically doctors analyzing CT scans, to more accurately and efficiently diagnose pulmonary embolism caused by blood clots. It takes CT images of the lungs (either 2D slices or 3D volumes) as input and highlights areas where pulmonary embolisms are present, reducing the need for manual identification. The primary users are physicians, radiologists, or medical researchers focused on improving diagnostic workflows for lung conditions.
No commits in the last 6 months.
Use this if you are a medical professional or researcher who needs to quickly and accurately identify pulmonary embolisms in CT scan images to aid in diagnosis and treatment planning.
Not ideal if you are looking for a general-purpose image analysis tool for non-medical images or if you require real-time, ultra-low-latency processing in a live clinical setting without further integration.
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Last pushed
Dec 27, 2023
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