The Medical ICT path investigates applications of ICT in the medical sector and provides competences to apply information technology to healthcare scenarios.
Areas of special interest are medical networking applications, signal processing for bioimaging, neurosciences, eHealth/mHealth, physical rehabilitation, and every other field where ICT applications can lead to an improvement in human quality of life.
The curriculum is closely linked to the Machine Learning for Healthcare track (which you can find here)
A list of the courses available in the Medical ICT curriculum for the 2022-2023 academic year is reported below.
The mandatory courses are Signal Processing for Life and Health (that includes Digital Signal Processing and Machine Learning) and e-Health.
The other courses can be freely chosen subject to some ground rules: at least 5 courses from the ICT subjects and 3 courses from the related subjects must be chosen.
Furthermore, a soft skill course must be selected, and students must pass the B2 English exam.
It is further advised to choose about 30 ECTS credits per semester to keep your workload balanced.
The Signal Processing for Life and Health course is mandatory.
It is made of 2 parts: Digital Signal Processing and Machine Learning.
Moreover, e-Health must also be taken mandatorily.
The course exploits basic signal analysis knowledge that the student is assumed to have acquired from previous studies to explore advanced concepts in the field of digital signal processing.
The course will review Z-transform, linear time-invariant systems, FIR/IIR filters, to investigate the design and usage of digital filters, interpolation/decimation of digital signals, frequency analysis of digital signals.
Practical application examples, useful in many areas of information engineering, will be provided.
Intelligent systems capable of automated reasoning are emerging as the most promising application of ICT.
The aim of this course is to provide fundamentals and basic principles of the machine learning problem as well as to introduce the most common techniques for regression and classification.
Both supervised and unsupervised learning will be covered, with a brief outlook into more advanced topics such as Support Vector Machines, neural networks and deep learning.
The course will be complemented by hands-on experience with Python programming.
e-Health deals with applications of ICT for healthcare and the course covers technologies for acquisition, transmission, and processing of data related to healthcare.
Particularly, it is divided into three units:
(1) data acqusition and analysis, e.g., for biosignals such as EEG and ECG,
(2) communication networks for e-health, e.g., body area sensor networks, and
(3) e-health applications, such as closed-loop interventions and pervasive continuous monitoring.
Hands-on laboratories will be proposed to experience signal acquisition, processing and body area network modelling, as well as guest lectures to meet technical experts and interact also with other professionals involved in the e-health field.
Select 5 courses from the following list
The course offers a guided tour of 3D computer vision, 3D graphics and machine learning tools to develop virtual and augmented reality applications.
After a description of imaging systems, the course reviews how to build a 3D model starting from 2D pictures and/or depth sensors, also by means of machine learning techniques, and finally the process of rendering real or virtual 3D models to standard images and 3D/AR devices.
Students will experience computer vision, deep learning and augmented reality techniques during lab sessions.
A beginners' tutorial on Unity will be provided as well.
This course is devoted to the interactions of light with living tissues and their technological applications to non-invasive biomedical imaging and treatments techniques.
The topics covered by this course include fundamentals of light and matter, light-tissue interactions (light scattering and absorption in tissues), principles of lasers and non-linear optics as preliminaries to later discuss applications such as optical microscopy, biomedical imaging, spectroscopic techniques, plasmonics and photonic biosensing.
The course deals with the analysis of biosignals, i.e., signals generated by human activity.
It will study extraction of information from quasi-periodic signals (denoising, segmentation);
application of clustering algorithms over biosignals to classify users and construct dictionaries for compact biometric datasets;
use of unsupervised learning to perform quantization;
statistical structures such as Bayesian networks and Hidden Markov Models, as well as supervised learning, for pattern and classification problems.
The course reviews statistical methods applied to large clinical datasets.
The main application is the evaluation of public hygiene data with statistical criteria to assess aspects such as the presence of epidemics, the effectiveness of a therapy, and possible directions and evolutions for treatments.
During the course, this kind of analysis will be also performed on real data taken from statistical datasets in different societal contexts, through laboratory sessions and group assignments.
The goal of the course is to provide the principles and tools needed to analyze and develop techniques for compression of multimedia data.
Both lossless and lossy coding techniques will be considered.
Methodologies for the evaluation of coding gain and rate distortion will also be discussed.
Finally, applications to present coding standards will be presented, such as data (ZIP), audio (MP3), pictures (JPEG), and video (MPEG) compression, as well as their implication to multimedia communications.
The course describes networking phenomena over many scenarios.
Although communication networks and the Internet are primary references, similar representations can be used for social networks, ecological systems, and epidemic diseases.
The course describes network generation models, and then community structures are reviewed, also outlining the applications to online social communities, brain networks, and biological systems.
The course covers the theory and practice of modern artificial neural networks, highlighting their relevance both for machine learning applications and for modeling human cognition and brain function.
Topics include single-neuron modeling and principles of neural encoding; supervised, unsupervised and reinforcement learning; feed-forward and recurrent networks; energy-based models; large-scale brain organization.
Theoretical discussion of various types of network architectures and learning algorithms is complemented by hands-on practices in the computer lab (PyTorch framework).
Course details will be available soon
Course details will be available soon
Select 3 courses from the following list
Course details will be available soon
The course tackles molecular-level problems with mathematical and computational thinking techniques.
Statistical modeling is used to interpret high-throughput data in genomics and transcriptonomics (signal processing of microarrays and RNA-sequencing).
Classes will discuss regulation models for molecular systems, the analysis of genetic polymorphism, functional annotation of data and personalized medicine.
The course discusses optical properties of matter at molecular scale.
Spectroscopy and radioscopy are introduced to study spectral and reflectance of materials, hyperspectral optical configurations and sensors.
The course also covers surface plasmon, Kretschman configuration, nanostructured plasmonic sensors, lithography, metamaterials for lenses.
Selected applications are presented to geology and agriculture (e.g. remote water detection), food industry, gas sensing, and medical diagnostics.
The course provides knowledge about analytical and synthetic engineering methodologies for the study of the central nervous system.
Covered topics include cerebral hemodynamics (MRI, arterial spin labeling), brain activation maps and connectivity, map generation from PET images, clustering, PCA and ICA, diffusion tensor MRI.
Students will understand potential and limitations of neuroimaging techniques in the study of pathophysiological brain processes.
Students will acquire knowledge on the analysis and manipulation of electroencephalography (EEG) signals for rehabilitation.
Furthermore, the course will provide skills to understand the fundamental concepts of brain-machine interfaces (BMIs) and the required ability to design and to develop the real-time analysis for BMI.
Finally, students will face the challenges to use BMI in order to control robotic devices.
The course will offer a carousel of data science methods for the validation of biomarkers in the context of neuroscience studies and for the investigation of brain disorders across the spectrum of neurological and mental health conditions.
Specifically, the course will present how biomarkers are used for the development and characterisation of new drugs targeting the central nervous system (including pharmacodynamics and pharmacokinetics properties) as well as for precision medicine applications.
This course expands programming skills already acquired by the students to give a special emphasis on scientific programming.
The students will be guided through the object-oriented programming paradigm to design and develop software in the Python language.
It will be given the competence to analyze formal correctness, computability, and complexity of a program, with a clear problem-solving purpose.
Course details will be available soon
This course discusses the basics of anthropometry and physiology of the musculoskeletal system, and reviews evaluation devices and methodologies, both hardware (e.g., sensors, motion capture systems, force platforms, pressure insoles, electromyography) and software (musculoskeletal simulation code), to design applications for comfort, safety, rehabilitation, orthoses, assistive technologies, prostheses and training or rehabilitation machines.
First-hand experimental session will also be held in the laboratories.
The lab aims to help students improve their oral communication through the study and practice of the elements contributing to successful communication.
The focus is on raising the students' awareness on the importance of verbal and non verbal language in interactions to make communication more effective.
The students will learn the meanings of body language and paralanguage (voice intonation, volume, etc), how they are used in different types of interactions (one-to-one, one-to-many, computer-mediated, etc.), and will have to apply them in a number of assigned tasks.
The lab requires the students' active participation in all class activities, aimed at applying the communication strategies learned.
This course will provide the foundations of the project management.
Traditional (such as the Project Management Institute approach) as well as more advanced techniques - such as the Agile Methodology - will be reviewed.
Special focus will be put on the methodologies more suited for the ICT environment.
Select further 18 ECTS credits from courses of this or another curriculum, or any course of the University of Padova coherently with your overall study plan.
You can select exams from the ICT subjects, the related ones or any other course from the University of Padova coherent with the study plan.
Students are asked to carry out a substantial individual project in their final year.
The project can be carried out either at the University of Padova (30 ECTS combining a 21 ECTS Final Project and a 9 ECTS Report), or in an external institution, such as an Industry or a Research Center, either national or international (30 ECTS combining a 21 ECTS Final Project and a 9 ECTS Internship).
It is also possible to do the internship in an external institution, and the final project at the University, though we suggest to carry out the whole work in a single place.