AI Agent for Diagnosis of Pancreatic CancerPersona da contattare:
ANDREA MOGLIAEmail:
andrea.moglia@polimi.it Corso di studio in Ingegneria Biomedica, Corso di Studio in Ingegneria Informatica
Descrizione
Descrizione:
Pancreatic cancer reported the lowest 5-year survival rate among all cancer types in the United States in the surveilled period 2014-2020. A clinical issue is represented by small pancreatic cancers (<2 cm), which may sometimes escape the attention of experts looking at computed tomography (CT) scans. This thesis focuses on the use of artificial intelligence (AI) to support clinicians in the diagnosis of pancreatic cancer. More specifically, the goal is to develop an AI agent that can automatically detect small pancreatic cancers on CT. This work continues the previous work conducted at DEIB and published on arXiv as a pre-print (Moglia A, et al., arXiv:2412.15925). The thesis provides for the possibility of completing an internship at NVIDIA.
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AI Agent to Compare Models for Medical Image SegmentationPersona da contattare:
ANDREA MOGLIAEmail:
andrea.moglia@polimi.it Corso di studio in Ingegneria Biomedica, Corso di Studio in Ingegneria Informatica
Pagina Web:
https://hal9000-lab.github.io/GMMIS-Survey/Descrizione
Descrizione:
The rapid advancement of generalist models in medical image segmentation has increasingly challenged specialist models, designed for a single task and trained on one or few datasets. This thesis focused on the development of an AI agent capable of systematically comparing generalist and specialist models across different organs and datasets. The objective is to address the needs of computer scientists, engineers, and medical doctors seeking to know which approach yields superior performance, to support them in the selection of the most suitable one. This work builds upon previous research conducted at DEIB and published in Information Fusion (Moglia A. et al., Inf. Fus., 2025, https://doi.org/10.1016/j.inffus.2025.103709). The thesis provides for the possibility of completing an internship at NVIDIA.
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Digital Twin Framework for Intrabody RF Wireless CommunicationsPersona da contattare:
SILVIA MURA, FRANCESCO LINSALATAEmail:
silvia.mura@polimi.it, francesco.linsalata@polimi.it Corso di studio in Ingegneria dell'Automazione, Ingegneria Biomedica, Ingegneria Elettrica, Ingegneria Elettronica, Corso di Studio in Ingegneria Informatica, Ingegneria delle Telecomunicazioni
Altri membri del gruppo di ricerca:
Maurizio MagariniDescrizione
Descrizione:
This thesis presents a digital twin framework for modeling and optimizing intrabody RF wireless communications across frequencies including millimeter-wave (mmWave) and terahertz (THz) bands. By integrating detailed anatomical and physiological data, the framework accurately replicates the complex electromagnetic environment within the human body. This enables predictive simulations of signal propagation, addressing key challenges such as attenuation, scattering, and absorption. The digital twin provides a non-invasive platform for designing and optimizing intrabody wireless systems, facilitating the advancement of high-performance networks for biomedical applications like implantable devices and real-time health monitoring.
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Intrabody Communication: Exploring different technologies for Biomedical Data TransmissionPersona da contattare:
SILVIA MURAEmail:
silvia.mura@polimi.it Corso di studio in Ingegneria dell'Automazione, Ingegneria Biomedica, Ingegneria Elettronica, Corso di Studio in Ingegneria Informatica, Ingegneria delle Telecomunicazioni
Altri membri del gruppo di ricerca:
Maurizio MagariniDescrizione
Descrizione:
Intrabody communication (IBC) is a promising technique for secure and energy-efficient data transmission within the human body, with applications in biomedical devices, wearable sensors, and implantable systems. The exploration of different transmission methods, including terahertz (THz) waves, galvanic coupling, and ultrasounds, presents an opportunity to optimize performance for diverse medical and health monitoring applications. This research seeks to analyze the feasibility and efficiency of these communication techniques through a combination of simulations and experimental validation.
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Real-Time Heart Signal Anomaly Detection and Prevention Using Radar TechnologyPersona da contattare:
SILVIA MURAEmail:
silvia.mura@polimi.it Corso di studio in Ingegneria dell'Automazione, Ingegneria Biomedica, Ingegneria Elettrica, Ingegneria Elettronica, Corso di Studio in Ingegneria Informatica, Ingegneria delle Telecomunicazioni
Altri membri del gruppo di ricerca:
Francesco LinsalataDescrizione
Descrizione:
This thesis focuses on developing algorithms to detect anomalies in heartbeat signals captured by radar, with an emphasis on decoupling cardiac signals from respiration to enhance detection accuracy. The research incorporates Integrated Sensing and Communication (ISAC), enabling radar systems to seamlessly combine physiological monitoring with communication functionality.
The student will design and implement methods for detecting or preventing heart rate anomalies, such as arrhythmias, and validate their performance through measurement campaigns using real radar data. The ultimate goal is to deliver a low-complexity, real-time solution for healthcare applications and preventative monitoring.
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Semantic Signal Reconstruction for Restoring Nerve Signal PropagationPersona da contattare:
SILVIA MURAEmail:
silvia.mura@polimi.it Corso di studio in Ingegneria dell'Automazione, Ingegneria Biomedica, Ingegneria Elettrica, Ingegneria Elettronica, Corso di Studio in Ingegneria Informatica, Ingegneria delle Telecomunicazioni
Altri membri del gruppo di ricerca:
Maurizio MagariniDescrizione
Descrizione:
This thesis explores a novel approach to restoring nerve function through semantic signal reconstruction, designed to act as a relay system for damaged nerves. By focusing on the "semantic meaning" of neural signals—the critical information necessary for physiological responses—this method bypasses the need to replicate the entire signal, enabling efficient and targeted signal propagation across injured sections. The work emphasizes low-complexity algorithms and real-time processing, making the approach suitable for implantable devices like cuff electrodes. These devices capture, interpret, and regenerate nerve signals, ensuring seamless communication despite nerve damage.
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Cellular infrastructure for weather monitoringPersona da contattare:
DARIO TAGLIAFERRIEmail:
dario.tagliaferri@polimi.it Corso di studio in Ingegneria dell'Automazione, Ingegneria Biomedica, Ingegneria Elettronica, Corso di Studio in Ingegneria Informatica, Ingegneria delle Telecomunicazioni
Descrizione
Descrizione:
The thesis aims at studying the potential of the cellular infrastructure for the retrieval and monitoring of rain.
The goal of the work is to investigate the potential of the cellular network to be use for weather forecasting, towards greener environments.
The student will learn how to model and process the cellular signals to estimate the rain precipitation rate, and validate the simulations on experimental data acquired with a 5G cellular base station.
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