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Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and…
The worlds of computing, communication, and storage have for a long time been treated separately, and even the recent trends of cloud computing, distributed computing, and mobile edge computing have not fundamentally changed the role of…
Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and…
Conventional medical image registration approaches directly optimize over the parameters of a transformation model. These approaches have been highly successful and are used generically for registrations of different anatomical regions.…
The MammoGrid project aims to prove that Grid infrastructures can be used for collaborative clinical analysis of database-resident but geographically distributed medical images. This requires: a) the provision of a clinician-facing…
Despite significant reliability efforts, large-scale cloud services inevitably experience production incidents that can significantly impact service availability and customer's satisfaction. Worse, in many cases one incident can lead to…
A real-time image reconstruction method for scanning transmission electron microscopy (STEM) is proposed. With an algorithm requiring only the center of mass (COM) of the diffraction pattern at one probe position at a time, it is able to…
Users of cloud platforms often must expend significant manual efforts in the deployment and orchestration of their services on cloud platforms due primarily to having to deal with the high variabilities in the configuration options for…
Open-set semantic mapping enables language-driven robotic perception, but current instance-centric approaches are bottlenecked by context-depriving and computationally expensive crop-based feature extraction. To overcome this fundamental…
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, data imbalances, and limited text annotations in datasets. Addressing these challenges requires…
The efficiency and service quality in a medical environment can be improved by using electronic documents (or e-docs) and digital signatures to speed up both doctors' activity and to provide in the same time easy retrieval and use of needed…
Backgr: Digital pathology images are increasingly used both for diagnosis and research, because slide scanners are nowadays broadly available and because the quantitative study of these images yields new insights in systems biology.…
The ever-increasing computation complexity of fastgrowing Deep Neural Networks (DNNs) has requested new computing paradigms to overcome the memory wall in conventional Von Neumann computing architectures. The emerging Computing-In-Memory…
Integrated healthcare systems require the transmission of medical images between medical centers. The presence of watermarks in such images has become important for patient privacy protection. However, some important issues should be…
Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we…
Segmentation models such as Segment Anything Model (SAM) and SAM2 achieve strong prompt-driven zero-shot performance. However, their training on natural images limits domain transfer to medical data. Consequently, accurate segmentation…
Artificial intelligence (AI) is increasingly being utilized to optimize magnetic resonance imaging (MRI) protocols. Given that image details are critical for diagnostic accuracy, optimizing MRI acquisition protocols is essential for…
Diffusion models (DMs) are a class of generative models that allow sampling from a distribution learned over a training set. When applied to solving inverse problems, the reverse sampling steps are modified to approximately sample from a…
The de-identification (deID) of protected health information (PHI) and personally identifiable information (PII) is a fundamental requirement for sharing medical images, particularly through public repositories, to ensure compliance with…
In this position paper we argue for standardizing how we share and process data in scientific workflows at the network-level to maximize step re-use and workflow portability across platforms and networks in pursuit of a foundational…