ما هي وظيفة bowang-lab/medsam؟

MedSAM هو إطار عمل للتعلم العميق مصمم لأتمتة تقسيم الهياكل التشريحية في الصور الطبية ثنائية وثلاثية الأبعاد. يوفر أدوات متخصصة لضبط أوزان التقسيم المدربة مسبقاً على مجموعات بيانات طبية مخصصة وتقييم دقة تلك التوقعات مقابل تسميات الحقيقة الأرضية (ground truth).

ما هي الميزات الرئيسية لـ bowang-lab/medsam؟

الميزات الرئيسية لـ bowang-lab/medsam هي: Medical Image Segmentations, Image Segmentations, Segmentation Model Training, SAM-Based Implementations, Medical Imaging Training Frameworks, Prompt-Guided Anatomical Segmentations, Segmentation Evaluation Metrics, Prompt-Based Segmentations.

ما هي البدائل مفتوحة المصدر لـ bowang-lab/medsam؟

تشمل البدائل مفتوحة المصدر لـ bowang-lab/medsam: wasserth/totalsegmentator — TotalSegmentator is a medical image segmentation tool and AI-driven organ segmenter designed to isolate anatomical… leejunhyun/image_segmentation — This project is a biomedical image segmentation framework and PyTorch computer vision library. It provides a deep… project-monai/monai — MONAI is a PyTorch-based deep learning framework and library specifically designed for healthcare imaging. It provides… fastai/course22 — This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It… fastai/course-v3 — This repository is a comprehensive educational program and deep learning framework designed to teach practical deep… zhengpeng7/birefnet — BiRefNet is a PyTorch image segmentation framework designed for high-precision binary mask generation. It functions as…

MedSAM - تقسيم الصور الطبية باستخدام الذكاء

Name: bowang-lab/medsam
Author: bowang-lab

Features

Medical Image Segmentations - Automates the identification and isolation of specific anatomical structures in 2D and 3D medical scans.
Image Segmentations - Implements a deep learning framework for partitioning 2D and 3D medical images into anatomical foreground and background pixels.
Segmentation Model Training - Optimizes model weights using single or distributed GPU clusters to learn complex image patterns in medical scans.
SAM-Based Implementations - Implements the Segment Anything Model architecture to automate anatomical structure segmentation in medical imagery.
Medical Imaging Training Frameworks - Offers a PyTorch-based training framework optimized for processing large-scale medical imaging datasets on single or distributed GPUs.
Prompt-Guided Anatomical Segmentations - Provides prompt-guided segmentation of anatomical structures using bounding boxes and point prompts.
Segmentation Evaluation Metrics - Computes quantitative performance metrics by comparing medical image predictions against expert ground truth labels.
Prompt-Based Segmentations - Enables isolation of anatomical structures using bounding boxes and point prompts to guide the segmentation model.
Model Fine-Tuning - Implements a training pipeline for optimizing pretrained segmentation weights on custom medical datasets.
Vision Model Fine-Tuning - Optimizes a Segment Anything Model on custom medical datasets to improve segmentation accuracy for specific anatomy.
Segmentation Model Testing - Quantifies segmentation accuracy by comparing model predictions against expert ground truth labels using standard metrics.
Distributed GPU Training - Ships a distributed GPU training pipeline to synchronize gradients across multiple cards for large-scale medical datasets.
Weight Remapping - Implements checkpoint weight transformation to ensure seamless compatibility between training and inference environments.
Medical Imaging Preprocessing Tools - Transforms various medical imaging file types into unified data structures for downstream preprocessing.
Model Checkpoint Converters - Includes utilities to transform trained model weights into compatible formats for streamlined deployment and inference.
Weight Extraction - Provides tools for extracting and standardizing model weights from checkpoints to ensure correct loading during inference.
Medical Segmentation Metrics - Computes accuracy metrics by comparing medical image segmentation predictions against expert ground truth labels.
Medical Dataset Formatting - Converts raw medical images and labels into optimized binary formats for efficient supervised learning.
Medical Image Normalization - Normalizes raw medical imaging files by adjusting window levels and resampling dimensions for ML pipelines.
Image Dimension Standardizations - Standardizes voxel size and image dimensions across diverse medical datasets for consistent model processing.
DICOM Windowing Adjusters - Adjusts window levels and width to normalize contrast and brightness of medical images across different scanners.

بدائل مفتوحة المصدر لـ MedSAM

مشاريع مفتوحة المصدر مشابهة، مرتبة حسب عدد الميزات المشتركة مع MedSAM.

wasserth/totalsegmentator
wasserth/TotalSegmentator
2,482عرض على GitHub
TotalSegmentator is a medical image segmentation tool and AI-driven organ segmenter designed to isolate anatomical structures from CT scans. It functions as a deep learning anatomy parser and quantitative radiomics analyzer, providing a framework for identifying diverse body tissues and bones to create precise anatomical masks. The system distinguishes itself through a comprehensive medical analysis suite that includes patient biometric estimation for demographics such as age, sex, weight, and height. It further provides specialized clinical index calculations and modality and phase classific
Python
عرض على GitHub2,482
leejunhyun/image_segmentation
LeeJunHyun/Image_Segmentation
3,063عرض على GitHub
This project is a biomedical image segmentation framework and PyTorch computer vision library. It provides a deep learning pipeline for isolating specific anatomical structures within medical imagery using pixel-level binary classification. The system utilizes an encoder-decoder neural architecture combined with attention-based feature refinement to highlight relevant anatomical regions and suppress background noise. The toolkit covers a full training workflow, including stochastic data augmentation for biomedical datasets, hyperparameter optimization, and model persistence for restoring pre
Python
عرض على GitHub3,063
project-monai/monai
Project-MONAI/MONAI
7,869عرض على GitHub
MONAI is a PyTorch-based deep learning framework and library specifically designed for healthcare imaging. It provides a suite of domain-specific neural network architectures, specialized loss functions, and preprocessing pipelines tailored for analyzing multi-dimensional medical data. The project distinguishes itself through a decentralized federated learning system that allows models to learn from datasets across multiple institutions without exchanging raw patient images. It also features AI-assisted medical image annotation tools and a standardized model bundling system to ensure consiste
Pythondeep-learninghealthcare-imagingmedical-image-computing
عرض على GitHub7,869
fastai/course22
fastai/course22
3,398عرض على GitHub
This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen
Jupyter Notebookdeep-learningfastaijupyter-notebooks
عرض على GitHub3,398

عرض جميع البدائل الـ 30 لـ MedSAM

بدائل مفتوحة المصدر لـ MedSAM

مشاريع مفتوحة المصدر مشابهة، مرتبة حسب عدد الميزات المشتركة مع MedSAM.

wasserth/totalsegmentator
wasserth/TotalSegmentator
2,482عرض على GitHub
TotalSegmentator is a medical image segmentation tool and AI-driven organ segmenter designed to isolate anatomical structures from CT scans. It functions as a deep learning anatomy parser and quantitative radiomics analyzer, providing a framework for identifying diverse body tissues and bones to create precise anatomical masks. The system distinguishes itself through a comprehensive medical analysis suite that includes patient biometric estimation for demographics such as age, sex, weight, and height. It further provides specialized clinical index calculations and modality and phase classific
Python
عرض على GitHub2,482
leejunhyun/image_segmentation
LeeJunHyun/Image_Segmentation
3,063عرض على GitHub
This project is a biomedical image segmentation framework and PyTorch computer vision library. It provides a deep learning pipeline for isolating specific anatomical structures within medical imagery using pixel-level binary classification. The system utilizes an encoder-decoder neural architecture combined with attention-based feature refinement to highlight relevant anatomical regions and suppress background noise. The toolkit covers a full training workflow, including stochastic data augmentation for biomedical datasets, hyperparameter optimization, and model persistence for restoring pre
Python
عرض على GitHub3,063
project-monai/monai
Project-MONAI/MONAI
7,869عرض على GitHub
MONAI is a PyTorch-based deep learning framework and library specifically designed for healthcare imaging. It provides a suite of domain-specific neural network architectures, specialized loss functions, and preprocessing pipelines tailored for analyzing multi-dimensional medical data. The project distinguishes itself through a decentralized federated learning system that allows models to learn from datasets across multiple institutions without exchanging raw patient images. It also features AI-assisted medical image annotation tools and a standardized model bundling system to ensure consiste
Pythondeep-learninghealthcare-imagingmedical-image-computing
عرض على GitHub7,869
fastai/course22
fastai/course22
3,398عرض على GitHub
This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen
Jupyter Notebookdeep-learningfastaijupyter-notebooks
عرض على GitHub3,398

عرض جميع البدائل الـ 30 لـ MedSAM

bowang-labMedSAM

MedSAM

bowang-labMedSAM

MedSAM

Features

بدائل مفتوحة المصدر لـ MedSAM

wasserth/TotalSegmentator

LeeJunHyun/Image_Segmentation

Project-MONAI/MONAI

fastai/course22

الأسئلة الشائعة

سجل النجوم

الأسئلة الشائعة

بدائل مفتوحة المصدر لـ MedSAM

wasserth/TotalSegmentator

LeeJunHyun/Image_Segmentation

Project-MONAI/MONAI

fastai/course22