Technologies / Artificial Intelligence

Deep Learning in biomedical data analysis with Python

This training focuses on advanced deep learning techniques in Python applied to biomedical data analysis. The program covers using TensorFlow and PyTorch for medical imaging, U-Net architecture for segmentation, transformers for clinical text processing (NLP), GANs for medical data augmentation, and federated learning enabling model training on hospital data without centralization. HIPAA-compliant deployment aspects are also covered.

Why choose this training?

Deep learning in Python is becoming a key tool in biomedical data analysis — from pathological image segmentation to clinical documentation processing. This training covers the practical application of TensorFlow and PyTorch for medical imaging, U-Net architecture for segmentation, transformers (BioBERT, ClinicalBERT) for clinical NLP, and GANs for data augmentation. The program also addresses federated learning and HIPAA-compliant deployment requirements.

After completing the training, participants will be able to: build deep learning models in TensorFlow and PyTorch for medical imaging, apply U-Net architecture and its variants for biomedical image segmentation, use transformers for clinical text analysis, and apply medical data augmentation techniques using GANs. These competencies directly translate into higher efficiency in IT project execution.

This training is particularly valuable for: Data Scientists specializing in deep learning, ML engineers in the medical and pharmaceutical sectors, Python developers building AI systems for healthcare.

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 deep learning models in TensorFlow and PyTorch for medical imaging
They will master U-Net architecture and its variants for biomedical image segmentation
They will gain the ability to apply transformers (BioBERT, ClinicalBERT) for clinical text analysis
They will learn medical data augmentation techniques using GANs
They will be able to design federated learning systems for hospital data
They will learn to deploy deep learning models in compliance with HIPAA and GDPR
They will understand the specifics of DICOM data processing and validation
They will develop skills in containerization and model deployment in clinical environments

Who is this training for?

Data Scientists specializing in deep learning

ML engineers in the medical and pharmaceutical sectors

Python developers building AI systems for healthcare

Clinical researchers with neural network experience

Medical imaging and radiology AI specialists

Bioinformaticians interested in deep learning techniques

AI systems architects in medical organizations

PhD students and researchers in the biomedical field

Prerequisites

Advanced knowledge of Python and deep learning
Experience with TensorFlow or PyTorch
Basic understanding of biomedical data and medical imaging
Familiarity with CNN and RNN network architectures

Training program

Environment setup for medical data (GPU, DICOM)
Medical image processing pipeline architecture
Comparing TensorFlow and PyTorch in biomedical contexts
Managing large image datasets (data loaders, caching)

U-Net architecture and its variants (Attention U-Net, U-Net++)
Organ and lesion segmentation
Segmentation metrics (Dice, IoU) and their clinical interpretation
Post-processing of segmentation results

BioBERT and ClinicalBERT — language models for medicine
Information extraction from clinical documentation
Named Entity Recognition for medical terminology
Clinical text and report classification

Generative Adversarial Networks for medical images
Synthetic patient data with privacy preservation
Quality validation of synthetic data
Augmentation strategies for rare pathologies

Federated learning — training models without data centralization
Federated learning system architecture in hospitals
HIPAA and GDPR requirements for AI systems in medicine
Containerization and model deployment in clinical environments

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 Python we recommend: Advanced knowledge of Python and deep learning; Experience with TensorFlow or PyTorch; Basic understanding of biomedical data and medical imaging.

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: Data Scientists specializing in deep learning; ML engineers in the medical and pharmaceutical sectors; Python developers building AI systems for healthcare.