Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Federico Fadda

Showing results (1-10 of 8) with videos related to

Pageof 1
Sort By:
Physical Review. E|June 25, 2020
Dynamics of a chiral swimmer sedimenting on a flat plateFederico Fadda, John Jairo Molina, Ryoichi Yamamoto
Soft Matter|March 1, 2023
The interplay between chemo-phoretic interactions and crowding in active colloidsFederico Fadda, Daniel A Matoz-Fernandez, René van Roij, et al.
Plos One|May 2, 2023
A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral regionSamantha Bove, Annarita Fanizzi, Federico Fadda, et al.
Scientific Reports|November 23, 2023
Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrenceAnnarita Fanizzi, Federico Fadda, Maria Colomba Comes, et al.
Cancer Medicine|October 31, 2023
Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2-positive breast cancer patients: A machine learning approachAnnarita Fanizzi, Agnese Latorre, Domenica Antonia Bavaro, et al.
Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)|October 22, 2024
Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practiceMichele Maddalo, Annarita Fanizzi, Nicola Lambri, et al.
Plos One|September 10, 2024
Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept studyAnnarita Fanizzi, Federico Fadda, Michele Maddalo, et al.
Computers in Biology and Medicine|March 20, 2024
Explainable 3D CNN based on baseline breast DCE-MRI to give an early prediction of pathological complete response to neoadjuvant chemotherapyMaria Colomba Comes, Annarita Fanizzi, Samantha Bove, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Physical Review. E|June 25, 2020
Dynamics of a chiral swimmer sedimenting on a flat plateFederico Fadda, John Jairo Molina, Ryoichi Yamamoto
Soft Matter|March 1, 2023
The interplay between chemo-phoretic interactions and crowding in active colloidsFederico Fadda, Daniel A Matoz-Fernandez, René van Roij, et al.
Plos One|May 2, 2023
A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral regionSamantha Bove, Annarita Fanizzi, Federico Fadda, et al.
Scientific Reports|November 23, 2023
Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrenceAnnarita Fanizzi, Federico Fadda, Maria Colomba Comes, et al.
Cancer Medicine|October 31, 2023
Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2-positive breast cancer patients: A machine learning approachAnnarita Fanizzi, Agnese Latorre, Domenica Antonia Bavaro, et al.
Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)|October 22, 2024
Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practiceMichele Maddalo, Annarita Fanizzi, Nicola Lambri, et al.
Plos One|September 10, 2024
Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept studyAnnarita Fanizzi, Federico Fadda, Michele Maddalo, et al.
Computers in Biology and Medicine|March 20, 2024
Explainable 3D CNN based on baseline breast DCE-MRI to give an early prediction of pathological complete response to neoadjuvant chemotherapyMaria Colomba Comes, Annarita Fanizzi, Samantha Bove, et al.
Pageof 1