Scopri il deep learning: reti neurali avanzate, applicazioni AI e il ruolo chiave di questa tecnologia nel futuro dell’innovazione.
This paper presents a complete pipeline for automatic diagnosis of various liver masses, consisting of components based on deep learning models for both the automatic segmentation of liver and its lesions, as well as the classification of masses into 5 classes, namely simple cysts, hemangiomas, hepatocellular carcinoma, calcifications and metastases.
This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence.
Considering the large number of patients with pulmonary symptoms admitted to the emergency department daily, it is essential to diagnose them correctly.
SenticLab ha vinto l'hackathon di MEDIMAG-IA con un sistema di intelligenza artificiale per riconoscere metastasi ossee e calcolarne l'attività metabolica.
Parkinson’s disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD.
SenticLab è nella Top 5 nella competizione KiTS 2023, avendo sviluppato un algoritmo per la segmentazione di immagini dei reni, per identificare cisti e tumori.
Using additional training data is known to improve the results, especially for medical image 3D segmentation where there is a lack of training material and the model needs to generalize well from few available data.
Oral capillaroscopy is a critical and non-invasive technique used to evaluate microcirculation. Its ability to observe small vessels in vivo has generated significant interest in the field. Capillaroscopy serves as an essential tool for diagnosing and prognosing various pathologies, with anatomic–pathological lesions playing a crucial role in their progression.
When interacting with fictional environments, the users' sense of immersion can be broken when characters act in mechanical and predictable ways.
Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays.