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We are inviting applications for a postgrad/postdoc position in the field of AI for surgery, with a specific focus on the following areas:
- Minimally Invasive Surgery: Endoscopy, Laparoscopic Surgery, and Robotics
- Prostate, Renal, and Adrenal Neoplasms
This position offers an exciting opportunity for young researchers interested in advancing the application of artificial intelligence in surgical procedures. The successful candidate will have the chance to work on cutting-edge projects and contribute to the development of AI-driven solutions in the context of minimally invasive surgery and the treatment of prostate, renal, and adrenal neoplasms.
Key Responsibilities:
- Conduct research in the field of AI and its applications in surgery. Collaborate with surgeons and medical professionals to develop AI-based tools and technologies.
- Analyze medical data, including imaging and diagnostic information.
- Contribute to the development of AI algorithms and models for surgical planning and decision support.
- Publish research findings in reputable scientific journals and conferences.
For inquiries or further information, please contact prof. Marco Grangetto, prof. Cristian Fiori.
The selection process will open in December.
EIDOS is very proud to contribute to ICCV 2023:
- Shannon Strikes Again! Entropy-Based Pruning in Deep Neural Networks for Transfer Learning Under Extreme Memory and Computation Budgets by Gabriele Spadaro; Riccardo Renzulli; Andrea Bragagnolo; Jhony H. Giraldo; Attilio Fiandrotti; Marco Grangetto; Enzo Tartaglione
Congratulations to Gabriele Spadaro for presenting his work during the poster session.
ICCV is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials.
EIDOS is very proud to contribute to ICIAP 2023:
- A differentiable entropy model for learned image compression by Presta, Alberto; Grangetto, Marco; Fiandrotti, Attilio; Tartaglione, Enzo
- Two is Better than One: Achieving High-Quality 3D Scene Modeling with a NeRF Ensemble by Di Sario, Francesco; Renzulli, Riccardo; Tartaglione, Enzo; Grangetto, Marco
- A Learnable EVC Intra Predictor using Masked Convolutions by Spadaro, Gabriele; Iacoviello, Roberto; Mosca, Alessandra; Valenzise, Giuseppe; Fiandrotti, Attilio
- Towards One-Shot PCB Component Detection with YOLO by Spadaro, Gabriele; Vetrano, Gaspare; Penna, Barbara; Serena, Antonio; Fiandrotti, Attilio
Congrats to Andrea Bragagnolo for the successful defense of his PhD thesis: “Exploring the Design and Implementation of Pruning Techniques for Deep Neural Networks”. The committee awarded his defense with honors.
The committee was composed by:
- Rossella Cancelliere, University of Turin
- Prof. Alasdair Newson, Télécom Paris, IP Paris
- Prof. Nicu Sebe, University of Trento
PhD Supervisors: Marco Grangetto (University of Turin), Andrea Basso (Synesthesia).
Congrats to Mirko Zaffaroni for his successful PhD defense. His thesis was title “Multimodal Learning and Feature Fusion Methodologies for Real Case Scenarios”.
The committee was composed by:
- Francesco Verdoja, Aalto University
- Prof. Laura Toni, UCL
- Prof. Attilio Fiandrotti, University of Turin
PhD Supervisor: Marco Grangetto (University of Turin)
A paper by Benoit Dufumier (NeuroSpin, CEA and Télécom Paris) and Carlo Alberto Barbano was accepted at ICML 2023 for poster presentation. A pre-print version is available at https://arxiv.org/abs/2206.01646.
The paper by Carlo Alberto Barbano “Contrastive learning for regression in multi-site brain age prediction” won one of two best poster awards at ISBI 2023. Full-text here: https://arxiv.org/abs/2211.08326.
The Co.R.S.A. (Covid Radiographic imaging System based on AI) project is getting momentum in the news:
RAI 3 TGR: March 11, 2023, Regional news
SKY: March 23, 2023, on Sky TG24
Project home: https://corsa.di.unito.it/
Half-Day workshop on the group collaboration on COVID-19 with the radiology unit at ASLTo3 in Pinerolo, Piemonte
The goal of the Co.R.S.A. (Covid Radiographic imaging System based on AI) project is to develop and deploy a cutting-edge AI system that can assist in the diagnosis of COVID pneumonia based on chest radiography (CXR) and evaluate its impact.
The project adopts an interdisciplinary approach aimed at the experimental development of a new AI technology for rapid diagnosis of COVID pneumonia from radiographic imaging. To this end, the project partners include Radiologists from the Department of Diagnostic Imaging of the Città della Salute e della Scienza (CSST) University Hospital of Turin and the ASL TO3, biomedical imaging experts from the Departments of Surgical Sciences and Oncology of the University of Turin, AI and image processing experts from the Department of Computer Science of the University of Turin, and a company specializing in the development of healthcare IT systems.
Project home: https://corsa.di.unito.it/
