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A fast data acquisition (DAQ) system for axion dark matter searches utilizing a microwave resonant cavity, also known as axion haloscope searches, has been developed with a two-channel digitizer that can sample 16-bit amplitudes at rates up…
The nervous system is today recognized to play an important role in the development of cancer. Indeed, neurons extend long processes (axons) that grow and infiltrate tumors in order to regulate the progression of the disease in a positive…
The purpose of this study was to show how advanced concepts of compact, lossless and "Time Of Flight" (TOF) capable electronics similar to those foreseen for the LHC and ILC experiments could be fairly and easily transferred to the medical…
Ultrasound (US) imaging is a vital adjunct to mammography in breast cancer screening and diagnosis, but its reliance on hand-held transducers often lacks repeatability and heavily depends on sonographers' skills. Integrating US systems from…
Traditional diagnostic methods like colonoscopy are invasive yet critical tools necessary for accurately diagnosing colorectal cancer (CRC). Detection of CRC at early stages is crucial for increasing patient survival rates. However,…
Ultrasound (US) is the most commonly used liver imaging modality worldwide. It plays an important role in follow-up of cancer patients with liver metastases. We present an interactive segmentation approach for liver tumors in US…
The complexity of the hardware and the amount of data collected during the PET imaging process require application of modern methods of efficient data organization and processing. In this article we will discuss the data structures and the…
Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response…
In the relentless efforts in enhancing medical diagnostics, the integration of state-of-the-art machine learning methodologies has emerged as a promising research area. In molecular biology, there has been an explosion of data generated…
Organ segmentation in Positron Emission Tomography (PET) plays a vital role in cancer quantification. Low-dose PET (LDPET) provides a safer alternative by reducing radiation exposure. However, the inherent noise and blurred boundaries make…
Melanoma detection is vital for early diagnosis and effective treatment. While deep learning models on dermoscopic images have shown promise, they require specialized equipment, limiting their use in broader clinical settings. This study…
The Data Acquisition System of the LZ experiment, the 10-tonne dark matter detector to be installed at the Sanford Underground Research Facility (SURF), will collect signals from 788 photomultiplier tubes (PMTs). Because the signals from…
Positron Emission Tomography (PET) is an important tool for studying Alzheimer's disease (AD). PET scans can be used as diagnostics tools, and to provide molecular characterization of patients with cognitive disorders. However, multiple…
Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In…
High-energy cosmic rays are one of the primary sources of information for scientists investigating the elementary properties of matter. The need to study cosmic rays, with energies thousands of times larger than those encountered in…
The synergistic interpretation of anatomical information from computed tomography (CT) and metabolic information from positron emission tomography (PET) is important to oncologic imaging. However, existing deep learning methods for PET/CT…
Tracking and characterizing the blood uptake process within solid pancreatic tumors and the subsequent spatio-temporal distribution of red blood cells are critical to the clinical diagnosis of the cancer. This systematic computational study…
This paper reports Deep LOGISMOS approach to 3D tumor segmentation by incorporating boundary information derived from deep contextual learning to LOGISMOS - layered optimal graph image segmentation of multiple objects and surfaces. Accurate…
Muon scattering tomography is a non-destructive imaging technique that utilizes the penetrating properties and multiple Coulomb scattering of muons to produce detailed internal images of objects. This information is crucial for various…
Advance in medical imaging is an important part in deep learning research. One of the goals of computer vision is development of a holistic, comprehensive model which can identify tumors from histology slides obtained via biopsies. A major…