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๐Ÿš€ GeoAI Tutorial 19: Train a Semantic Segmentation Model for Objection Detection from Remote Sensing Imagery ๐ŸŒ Leverage any PyTorch segmentation model from โ€” with hundreds of image encoders & pretrained weights available. ๐ŸŽฅ Watch the full tutorial here: ๐Ÿ““ Explore the notebook: ๐Ÿ› ๏ธ Check out the GitHub repository:...

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๐Ÿš€ The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. ๐Ÿ“„ Paper: ๐Ÿ’ป Code: **Highlights:** 1. SAM2 doesnโ€™t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesnโ€™t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! ๐Ÿ”ง Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch

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Project Launch ๐Ÿš€: mukh v0.1.14 - The Face Analysis Library Hello Everyone, I am happy to launch my first python package "mukh". Mukh (เคฎเฅเค–, meaning "face" in Sanskrit) is a comprehensive face analysis library that provides unified APIs for various face-related tasks. It simplifies the process of working with multiple face analysis models through a consistent interface. --- ๐Ÿ“Œ Features: ๐Ÿฅธ DeepFake Detector: First Python package featuring an Ensemble of multiple models ๐ŸŽฏ Unified API: Single, consistent API for multiple face analysis tasks like face detection and reenactment ๐Ÿ”„ Model Flexibility: Support for multiple models per task ๐Ÿ› ๏ธ Custom Pipelines: Optimized preprocessing and model combinations --- ๐Ÿ“Œ Currently Supported Tasks: 1๏ธโƒฃ Face Detection 2๏ธโƒฃ Face Reenactment with Source Image and Driving Video 3๏ธโƒฃ Deepfake Detection for Image and Video 4๏ธโƒฃ Deepfake Detection Pipeline - Ensemble of multiple models --- ๐Ÿ“Œ Open Source Contributions: I am opening up multiple tasks in the issues section of mukh including a bunch of `good-first-issues`. My goal is to help a number of beginners get started with their contributions, I will be personally guiding across completing these tasks so keep a check on the issues section. The link to the GitHub repository and detailed documentation is available below ๐Ÿ‘‡ in the reply section, do give it a โญ๏ธ to support the project! --- Special Mentions in the video: Ayush Chaurasia adi unni hsr hacker house Nimisha Chanda @ElonMastikhor Ctrl+Vibe

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