The Priberam Machine Learning Lunch Seminars are a series of informal meetings which occur every two weeks at Instituto Superior Técnico, in Lisbon. It works as a discussion forum involving different research groups, from IST and elsewhere. Its participants are interested in areas such as (but not limited to): statistical machine learning, signal processing, pattern recognition, computer vision, natural language processing, computational biology, neural networks, control systems, reinforcement learning, or anything related (even if vaguely) with machine learning.

The seminars last for about one hour (including time for discussion and questions) and revolve around the general topic of Machine Learning. The speaker is a volunteer who decides the topic of his/her presentation. Past seminars have included presentations about state-of-the-art research, surveys and tutorials, practicing a conference talk, presenting a challenging problem and asking for help, and illustrating an interesting application of Machine Learning such as a prototype or finished product.

Presenters can have any background: undergrads, graduate students, academic researchers, company staff, etc. Anyone is welcome both to attend the seminar as well as to present it. Ocasionally we will have invited speakers. See below for a list of all seminars, including the speakers, titles and abstracts.

Note: The seminars are held at lunch-time, and include delicious free food.

Feel free to join our mailing list, where seminar topics are announced beforehand. You may also visit the group webpage. Anyone can attend the seminars. If you would like to present something, please send us an email.

The seminars are usually held every other Tuesday, from 1 PM to 2 PM, at the IST campus in Alameda. This sometimes changes due to availability of the speakers, so check regularly!

Tuesday, May 3rd 2022, 13h00 - 14h00

Wilson Silva (INESC TEC)

Explainable Artificial Intelligence and its role in supporting medical diagnosis

Location (webinar): (Zoom)


The use of deep learning algorithms in the clinical context is hindered by their lack of interpretability. One way of increasing the acceptance of such complex algorithms is by providing explanations of the decisions through the presentation of similar examples. Besides helping to understand model behaviour, the presentation of similar disease-related examples, also supports the decision-making process of the radiologist or clinician under challenging diagnosis scenarios. In this talk, the speaker will discuss and present his work on strategies to provide decisions and case-based explanations in the medical domain. Particularly, he will discuss the work developed in several clinical applications, such as, aesthetic evaluation of breast cancer treatments, melanoma detection in dermoscopic images, and pleural effusion diagnosis in chest x-ray images.


Bio: Wilson Silva is a PhD Candidate in Electrical and Computer Engineering at the Faculty of Engineering of the University of Porto (FEUP) and a research assistant at INESC TEC, where he is associated with the Visual Computing and Machine Intelligence and Breast research groups. Currently, Wilson is also an Invited Teaching Assistant at FEUP, where he teaches courses related to Machine Learning, Computer-aided Diagnosis, and Programming. He holds an integrated master’s degree (BSc + MSc) in Electrical and Computer Engineering obtained from FEUP in 2016. He was a visiting master’s student for one year at the Karlsruhe Institute of Technology (KIT, Karlsruhe, Germany), and a visiting PhD student for six months at Inselspital (University Hospital Bern, Bern, Switzerland). His main research interests include Machine Learning and Computer Vision, with a particular focus on Explainable Artificial Intelligence and Medical Image Analysis.

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