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Marco Colussi

Ph.D. in Artificial Intelligence for Medical Imaging

University of Milan

I’m a PhD student at Everyware Lab (UniMi), my reseach intrests include machine learning and its applications in medical imaging, from pathology classification, to anomaly detection and reconstruction. Currently undergoing a PhD visiting at ÈTS Montreal.

Education

PhD visiting in Computer Science

ÈTS Montreal

September 2023 – Present Montreal

PhD in Computer Science

University of Milan

October 2021 – Present Milan

MSc in Computer Science

University of Milan

October 2019 – October 2021 Milan

BSc in Computer Science

University of Milan

October 2015 – October 2019 Milan

Featured Publications

Marco Colussi, Gabriele Civitarese, Dragan Ahmetovic, Claudio Bettini, Roberta Gualtierotti, Flora Peyvandi, Sergio Mascetti

February, 2023 ISWA

Ultrasound detection of subquadricipital recess distension

The paper proposes two approaches for automatically detecting joint bleeding in patients with hemophilia using ultrasound imaging. The experimental evaluation shows that the multi-task approach has higher accuracy, but with a slightly lower mean IoU value. The study suggests that computer-aided diagnosis tools can be useful for diagnosing joint bleeding in hemophilia patients.

Recent Publications

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Mattia Giovanni Campana, Marco Colussi, Franca Delmastro, Sergio Mascetti, Elena Pagani (2024). A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images. PMC.

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Marco Colussi, Sergio Mascetti, Dragan Ahmetovic, Gabriele Civitarese, Marco Cacciatori, Roberta Gualtierotti, Flora Peyvandi, Claudio Bettini (2023). GAJA - Guided self-Acquisition of Joint ultrAsound images. ASMUS23.

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Roberta Gualtierotti, Sara Arcudi,, Alessandro Ciavarella, Marco Colussi, Sergio Mascetti, Claudio Bettini, Flora Peyvandi (2022). A Computer-Aided Diagnosis Tool for the Detection of Hemarthrosis By Remote Joint Ultrasound in Patients with Hemophilia. Blood.

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Marco Colussi, Stavros Ntalampiras (2020). Interpreting deep urban sound classification using Layer-wise Relevance Propagation.

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