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nwirandx/medicalnet-resnet3d200
medicalnet-resnet3d200 is a machine learning model from nwirandx. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
The MedicalNet project aggregated the dataset with diverse modalities, target organs, and pathologies to to build relatively large datasets. Based on this dataset, a series of 3D-ResNet pre-trained models and correspo…
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From the Hugging Face model README
The MedicalNet project aggregated the dataset with diverse modalities, target organs, and pathologies to to build relatively large datasets. Based on this dataset, a series of 3D-ResNet pre-trained models and corresponding transfer-learning training code are provided.
This repository is an unofficial implementation of Tencent's Med3D model (Med3D: Transfer Learning for 3D Medical Image Analysis), originally developed for 3d segmentation tasks. It has been adapted for classification tasks using the 3D-ResNet backbone and made compatible with the Hugging Face library.
MedicalNet is released under the MIT License (refer to the LICENSE file for details).
If you use this code or pre-trained models, please cite the following:
@article{chen2019med3d,
title={Med3D: Transfer Learning for 3D Medical Image Analysis},
author={Chen, Sihong and Ma, Kai and Zheng, Yefeng},
journal={arXiv preprint arXiv:1904.00625},
year={2019}
}
from transformers import AutoConfig, AutoModelForImageClassification
import torch
config = AutoConfig.from_pretrained(
'nwirandx/medicalnet-resnet3d200',
trust_remote_code=True
)
# use a model from scratch
# model = AutoModelForImageClassification.from_config(
# config,
# trust_remote_code=True
# )
# use pretrained model
model = AutoModelForImageClassification.from_pretrained(
'nwirandx/medicalnet-resnet3d200',
trust_remote_code=True
)
x = torch.randn(1, 1, 64, 64, 64) # Example 3D volume
outputs = model(x)
Original MedicalNet Series (Tencent on Hugging Face)
Unofficial Versions of the MedicalNet Classification Model Series