Zobrazeno 1 - 10
of 180
pro vyhledávání: '"Gori, Pietro"'
Contrastive Analysis is a sub-field of Representation Learning that aims at separating common factors of variation between two datasets, a background (i.e., healthy subjects) and a target (i.e., diseased subjects), from the salient factors of variati
Externí odkaz:
http://arxiv.org/abs/2402.11928
Contrastive Analysis (CA) deals with the discovery of what is common and what is distinctive of a target domain compared to a background one. This is of great interest in many applications, such as medical imaging. Current state-of-the-art (SOTA) met
Externí odkaz:
http://arxiv.org/abs/2401.17776
Autor:
Cardiello, António, Gori, Pietro
Publikováno v:
Pessoa Plural, Iss 10, Pp 578-605 (2016)
In a note to G.R.S. Mead's Quests Old and New, where he found a section devoted to Hans Vaihinger's main ideas, Fernando Pessoa reflects on the consequences of the fictionalist approach to both our perception of the I and the value of consciousness.
Externí odkaz:
https://doaj.org/article/9ec1491f75f9495a84107e7307c66c14
Autor:
Vétil, Rebeca, Abi-Nader, Clément, Bône, Alexandre, Vullierme, Marie-Pierre, Rohé, Marc-Michel, Gori, Pietro, Bloch, Isabelle
We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR features using a VAE while enforcing their independence with HCR features by minimizing their mutual i
Externí odkaz:
http://arxiv.org/abs/2308.11389
Early diagnosis of prostate cancer is crucial for efficient treatment. Multi-parametric Magnetic Resonance Images (mp-MRI) are widely used for lesion detection. The Prostate Imaging Reporting and Data System (PI-RADS) has standardized interpretation
Externí odkaz:
http://arxiv.org/abs/2308.09542
Contrastive Analysis VAE (CA-VAEs) is a family of Variational auto-encoders (VAEs) that aims at separating the common factors of variation between a background dataset (BG) (i.e., healthy subjects) and a target dataset (TG) (i.e., patients) from the
Externí odkaz:
http://arxiv.org/abs/2307.06206
Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e.g., radiological scores). Access to high-confidence labels, such as histology-based diagnoses, is rare and costly. Pretraining strategies, like co
Externí odkaz:
http://arxiv.org/abs/2307.04617