Course Syllabus

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L' obiettivo del corso è di fornire i principi fisici e i metodi di elaborazione alla base dei sistemi di acquisizione dei segnali e immagini biomedici per lo sviluppo di modelli di intelligenza artificiale applicati a tali sistemi che supportino la decisione medica nella prevenzione, screening, diagnosi e terapia di pazienti a rischio di patologie multifattoriali complesse.
Le lezioni teoriche sono integrate con esercitazioni pratiche in aula durante le quali saranno forniti dataset di segnali e immagini biomedici per applicare i principi teorici nello sviluppo di modelli di intelligenza artificiale a supporto della decisione medica.

Principi fisici e metodi di elaborazione dei sistemi di acquisizione dei segnali e immagini biomedici per lo sviluppo di modelli di intelligenza artificiale affidabili e comprensibili applicati che supportino la decisione medica.

Lezioni teoriche integrate con esercitazioni pratiche in aula per lo sviluppo di modelli di intelligenza artificiale a supporto della decisione medica.

-Biomedical signals: Electrocardiography/Electroencelography/Electromiography/functional NIRS

  • Machine learning and deep learning systems for signal-guided personalized predictive medicine
    -Biomedical imaging: Ultrasonography/Radiography/Computerized Tomography/ Mammography/MRI, mpMRI, fMRI/Positron Emission Tomography/Hybrid systems
    -Biomedical imaging in image-guided radiotherapy
    -Biomedical imaging for lesion detection and semantic segmentation
  • Radiomic/radiogenomic modelling for screening and diagnosis
    -Radiomic/radiogenomic modelling for treatment
    -Machine learning and deep learning systems for explainable image-guided personalized predictive medicine (supervised/unsupervised learning)

Livello medio-alto di programmazione in Matlab o Python

Lezioni frontali ed esercitazioni in aula
E’ RICHIESTA LA FREQUENZA
Nel periodo di emergenza Covid-19 le lezioni si svolgeranno in lezioni video sincrone

Appunti, software, dati e articoli scientifici forniti agli studenti durante il corso.

Primo semestre.

L' esame consiste in un colloquio orale e 2 verifiche mediante esercitazioni in aula volti a verificare il livello di conoscenza dello studente degli argomenti trattati durante il corso.
Nel periodo di emergenza Covid-19 gli esami orali saranno solo telematici. Verranno svolti utilizzando la piattaforma WebEx e nella pagina e-learning dell'insegnamento verrà riportato un link pubblico per l'accesso all'esame di possibili spettatori virtuali.

Al termine della lezione in aula il docente e' disponibile a ricevere gli studenti per 1 h

SALUTE E BENESSERE
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The aim of the course is to provide the physical principles and processing methods underlying biomedical signal and image acquisition systems for the development of artificial intelligence models applied to that support medical decision-making in the prevention, screening, diagnosis and therapy of patients at risk of complex multifactorial diseases.
Theoretical lessons are integrated with practical exercises in the classroom during which datasets of biomedical signals and images will be provided to apply the theoretical principles in the development of artificial intelligence models to support medical decision.

Physical principles and processing methods of biomedical image and signal acquisition systems for the development of trustworth and explainable artificial intelligence models that support medical decision.

Theoretical lessons integrated with practical exercises in the classroom for the development of artificial intelligence models to support medical decisions.

  • Biomedical signals: Electrocardiography / Electroencelography / Electromyography / functional NIRS
  • Machine learning and deep learning systems for signal-driven personalized predictive medicine
  • Biomedical imaging: ultrasound / radiography / computed tomography / mammography / MRI, mpMRI, fMRI / positron emission tomography / hybrid systems
  • Biomedical imaging in image-guided radiotherapy
  • Biomedical imaging for lesion detection and semantic segmentation
  • Radiomic / radiogenomic modeling for screening and diagnosis
  • Radiomic / radiogenomic modeling for treatment
    -Machine learning and deep learning systems for explainable image-guided personalized predictive medicine (supervised / unsupervised learning)

Medium-High level of programming in Matlab or Python

Lectures and classroom exercises

Notes, software, data and scientific articles provided to students during the course.

First semester.

The exam consists of an oral and 2 tests through classroom exercises aimed at verifying the student's level of knowledge of the topics covered during the course.
During the Covid-19 emergency period, the exams will either be telematic only. They will be carried out using the WebEx public platform and on the e-learning page of the course a link will be shown for access to the exam of possible virtual spectators.

At the end of the classroom lesson the teacher is available to receive students for 1 hour

GOOD HEALTH AND WELL-BEING
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Key information

Field of research
FIS/07
ECTS
6
Term
Second semester
Activity type
Mandatory to be chosen
Course Length (Hours)
64
Degree Course Type
2-year Master Degreee
Language
English

Staff

    Teacher

  • IC
    Isabella Castiglioni

Students' opinion

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Bibliography

Find the books for this course in the Library

Enrolment methods

Manual enrolments
Self enrolment (Student)

Sustainable Development Goals

GOOD HEALTH AND WELL-BEING - Ensure healthy lives and promote well-being for all at all ages