Technologies / Artificial Intelligence

Deep Learning in biomedical data analysis with R

This training focuses on advanced deep learning techniques implemented in the R environment using keras and torch packages. The program covers medical image analysis with CNN networks, patient time series processing with RNN, transfer learning for small medical datasets, and model explainability methods. Special attention is given to regulatory validation required when deploying models in clinical settings.

Why choose this training?

Deep learning is revolutionizing biomedical data analysis — from diagnostic imaging to disease progression modeling. This training focuses on the practical application of keras and torch packages in the R environment to build CNN networks for pathology analysis, RNN for patient time series, and transfer learning techniques essential when working with small medical datasets. Special emphasis is placed on model explainability and regulatory validation.

After completing the training, participants will be able to: build and train CNN networks in R for medical image analysis, model patient time series using RNN (LSTM, GRU), apply transfer learning on small medical datasets, and use deep learning explainability methods relevant in clinical contexts. These competencies directly translate into higher efficiency in IT project execution.

This training is particularly valuable for: biomedical data analysts working in R, bioinformaticians and biostatisticians, clinical researchers with R experience.

What sets our approach apart?

At EITT, we believe the best learning happens through practice. During 3 days of intensive training, participants work on real-world examples and scenarios, ensuring not only theoretical understanding but above all the ability to apply it in practice.

With over 2,500 trainings in our portfolio and a 4.8/5 rating from participants, EITT is a trusted partner in competency development for organizations of all sizes. Our trainers are practitioners with years of experience who share current knowledge and proven solutions.

Looking for training tailored to your team’s needs? Contact us — we’ll prepare a program customized to your requirements.

Benefits

Participants will learn to build and train CNN networks in R for medical image analysis
They will master patient time series modeling techniques using RNN (LSTM, GRU)
They will gain the ability to apply transfer learning on small medical datasets
They will learn deep learning explainability methods relevant in clinical contexts
They will be able to conduct regulatory model validation in accordance with industry requirements
They will learn to process and augment image data in the R ecosystem
They will understand the specifics of deploying deep learning models in clinical environments
They will develop skills in documenting models for regulatory bodies

Who is this training for?

Biomedical data analysts working in R

Bioinformaticians and biostatisticians

Clinical researchers with R experience

Medical imaging specialists

Data Scientists in pharmaceutical and biotech companies

ML engineers interested in medical applications

Academic researchers at universities and research institutes

Analytics consultants in the healthcare sector

Prerequisites

Advanced knowledge of the R language
Basic understanding of neural networks and deep learning
Experience working with biomedical or clinical data
Familiarity with image processing fundamentals

Training program

Keras and torch packages for R — setup and architecture
Image data processing and augmentation in R
Preparing medical data (images, time series, clinical tables)
GPU environment management from R

Convolutional networks for pathology classification
Histopathological image segmentation
Anomaly detection in radiological images
Data augmentation for small medical image datasets

Disease progression modeling with LSTM and GRU
Clinical event prediction based on EHR data
Biomedical signal analysis (ECG, EEG)
Handling missing data in time series

Pretrained models for medical imaging
Fine-tuning on specialized datasets
Few-shot learning techniques in biomedical contexts
Validation strategies with limited sample sizes

Deep learning explainability methods (Grad-CAM, saliency maps)
Model documentation for regulatory bodies
Clinical validation and A/B testing in medical settings
FDA and MDR requirements for software as a medical device

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

What are the prerequisites for this training?

For Deep Learning in biomedical data analysis with R we recommend: Advanced knowledge of the R language; Basic understanding of neural networks and deep learning; Experience working with biomedical or clinical data.

What is the format and duration of this training?

The training lasts 3 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Biomedical data analysts working in R; Bioinformaticians and biostatisticians; Clinical researchers with R experience.