This is the Linux app named Deep Learning for Medical Applications whose latest release can be downloaded as Deep-Learning-for-Medical-Applicationssourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Deep Learning for Medical Applications with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start the OnWorks Linux online or Windows online emulator or MACOS online emulator from this website.
- 5. From the OnWorks Linux OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application, install it and run it.
SCREENSHOTS
Ad
Deep Learning for Medical Applications
DESCRIPTION
Deep-Learning-for-Medical-Applications is a repository that compiles deep learning methods, code implementations, and examples applied to medical imaging and healthcare data. The project addresses domain-specific challenges like segmentation, classification, detection, and multimodal data (e.g. MRI, CT, X-ray) using state-of-the-art architectures (e.g. U-Net, ResNet, GAN variants) tailored to medical constraints (small datasets, annotation costs, class imbalance). It includes Jupyter notebooks, model architectures, data preprocessing pipelines, and evaluation scripts specific to medical imaging tasks. The repository may also contain domain-specific modules: loss functions like Dice, focal loss, metrics such as sensitivity/recall/IoU, and visualization utilities for overlaying segmentation masks.
Features
- Model architectures (e.g. U-Net, ResNet, GAN variants) specialized for medical imaging
- Preprocessing pipelines and augmentation techniques for medical data
- Loss functions and metrics suited to segmentation, class imbalance, e.g. Dice, focal loss
- Evaluation and visualization utilities for overlaying predictions on medical images
- Jupyter notebooks showing end-to-end workflows in medical AI tasks
- Emphasis on reproducibility, careful validation, and domain-aware design
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/deep-learning-med-app.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
