ML CODE FOR GAN -- 2
Budget: £10 – £200 GBP
I am looking for a skilled ML developer who can implement a GAN model for my academic research project. The specific GAN model I am looking to implement is a GAN for chest X-rays, with a focus on overcoming class imbalance and data augmentation.
Skills and experience required:
- Strong understanding and experience with GAN models, specifically DCGAN, WGAN, or CGAN
- Experience working with medical imaging datasets, particularly chest X-rays
- Proficiency in data preprocessing techniques to handle class imbalance and generate synthetic data
- Familiarity with machine learning frameworks such as TensorFlow or PyTorch
- Knowledge of evaluation metrics for GAN models in the medical domain
The project will involve using an existing dataset for training the GAN model, which I already have available. The main goal of this project is to develop a GAN model that can generate realistic and diverse chest X-ray images, while also addressing the class imbalance issue and incorporating data augmentation techniques, which enhances the lung nodule detection in the images using RF and SVM as classifier. and calculate their efficiency using ROC, f1 scores.. and then transfer the same model using transfer learning to the CT images dataset and calculate efficiency in detecting lung nodules using RF and SVM
If you have experience in this field and are interested in contributing to academic research, please submit your proposal. This is a great opportunity to apply your ML skills to a real-world problem and make a valuable contribution to the medical imaging field.
Skills and experience required:
- Strong understanding and experience with GAN models, specifically DCGAN, WGAN, or CGAN
- Experience working with medical imaging datasets, particularly chest X-rays
- Proficiency in data preprocessing techniques to handle class imbalance and generate synthetic data
- Familiarity with machine learning frameworks such as TensorFlow or PyTorch
- Knowledge of evaluation metrics for GAN models in the medical domain
The project will involve using an existing dataset for training the GAN model, which I already have available. The main goal of this project is to develop a GAN model that can generate realistic and diverse chest X-ray images, while also addressing the class imbalance issue and incorporating data augmentation techniques, which enhances the lung nodule detection in the images using RF and SVM as classifier. and calculate their efficiency using ROC, f1 scores.. and then transfer the same model using transfer learning to the CT images dataset and calculate efficiency in detecting lung nodules using RF and SVM
If you have experience in this field and are interested in contributing to academic research, please submit your proposal. This is a great opportunity to apply your ML skills to a real-world problem and make a valuable contribution to the medical imaging field.