The lack of extensive, varied, and thoroughly annotated datasets impedes the advancement of artificial intelligence (AI) for medical applications, especially colorectal cancer detection. Models trained with limited diversity often display biases, esp...
Background: CT scans are often the first and only form of brain imaging that is performed to inform treatment plans for neurological patients due to its time- and cost-effective nature. However, MR images give a more detailed picture of tissue struct...
Deep learning approaches play a crucial role in computer-aided diagnosis systems to support clinical decision-making. However, developing such automated solutions is challenging due to the limited availability of annotated medical data. In this study...
Acquiring relevant, high-quality, and heterogeneous medical images is essential in various types of automated analysis, used for a variety of downstream data augmentation tasks. However, a large number of real image samples are expensive to obtain, e...
Background/Objectives: Generative Models have revolutionized the synthesis of complex, realistic medical images. However, obtaining annotated, high-quality datasets is challenging and expensive due to privacy concerns, high human annotation costs, an...
Medical images provide a visual representation of the internal structure of the human body. Injecting a contrast agent can increase the contrast of diseased tissues and assist in the accurate identification and assessment of conditions. Considering t...
Background: Two-stage coarse-to-fine architectures are commonly used in medical image synthesis and refinement. However, when the distributional gap between coarse inputs and high-resolution targets is large, the second-stage generator may produce un...
With the advent of generative AI, medical imaging has been revolutionized, allowing for unprecedented capabilities in data generation, volumetric reconstruction, and clinical decision support. Although significant advances have been achieved in two-d...
Objective: With the strengths of deep learning, computer-aided diagnosis (CAD) is a hot topic for researchers in medical image analysis. One of the main requirements for training a deep learning model is providing enough data for the network. However...
Magnetic resonance imaging (MRI), a widely adopted modality in clinical practice, enables the acquisition of multi-contrast images from the same anatomical structure, commonly referred to as multimodal images. Integrating these diverse modalities is...
Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for earl...
Background/Objectives: Ulcerative Colitis (UC) lacks longitudinal visual data, which limits both disease progression modeling and the effectiveness of computer-aided diagnosis systems. These systems are further constrained by sparse intermediate dise...
Semiconductor nanocrystals with a perovskite crystal structure, both fully inorganic and hybrid, have found broad application as scintillation materials over the past decade, owing to their high absorption capability, fast response time, tunable opti...
Three-dimensional medical images, such as those obtained from MRI scans, offer a comprehensive view that aids in understanding complex shapes and abnormalities better than 2D images, such as X-ray, mammogram, ultrasound, and 2D CT slices. However, MR...
Background/Objectives: Reviewing the entire history of imaging exams of a single patient’s records is an essential step in clinical practice, but it is time and resource consuming, with potential negative effects on workflow and on the quality...
We report the design, fabrication and characterization of a carbon nanotube enabled open-type X-ray system for medical imaging. We directly grew the carbon nanotubes used as electron emitter for electron gun on a non-polished raw metallic rectangular...
Generative adversarial networks (GANs) typically require large datasets for effective training, which poses challenges for volumetric medical imaging tasks where data are scarce. This study addresses this limitation by extending adaptive discriminato...
Background: The limited availability of cardiac MRI data significantly constrains deep learning applications in cardiovascular imaging, necessitating innovative approaches to address data scarcity while preserving critical cardiac anatomical features...
Obtaining multiple MRI contrasts for each patient prolongs scan acquisition time, increases healthcare costs, and may not always be feasible due to patient specific constraints. Deep learning-based MRI contrast synthesis offers a potential solution,...
7 June 2024
Current computer vision models require a significant amount of annotated data to improve their performance in a particular task. However, obtaining the required annotated data is challenging, especially in medicine. Hence, data augmentation technique...
11 September 2023
CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning m...
Due to recent developments in deep learning and artificial intelligence, the healthcare industry is currently going through a significant upheaval. Despite a considerable advance in medical imaging and diagnostics, the healthcare industry still has a...
Synthesizing multi-phase contrast-enhanced CT images from non-contrast CT (NCCT) may provide complementary phase-specific cues for preliminary assessment. After rigorous clinical validation, such images could provide clinical decision support or aid...
Background: Diffusion-weighted imaging (DWI), a pivotal component of multiparametric magnetic resonance imaging (mpMRI), plays a pivotal role in the detection, diagnosis, and evaluation of gastric cancer. Despite its potential, DWI is often marred by...
The rapid development of deep learning in medical imaging has significantly enhanced the capabilities of artificial intelligence while simultaneously introducing challenges, including the need for vast amounts of training data and the labor-intensive...
This paper introduces a new method for real-time high-density impulsive noise elimination applied to medical images. A double process aimed at the enhancement of local data composed of Nested Filtering followed by a Morphological Operation (NFMO) is...
Background: The clinical imperative to reduce patient ionizing radiation exposure during diagnosis and treatment planning necessitates robust, high-fidelity synthetic imaging solutions. Current cross-modal synthesis techniques, primarily based on GAN...
Multi-modal MRI provides essential anatomical and pathological information for accurate brain tumor segmentation. However, deep learning-based segmentation methods are hampered by limited annotated data and incomplete modality acquisition in clinical...
Medical image processing has been highlighted as an area where deep-learning-based models have the greatest potential. However, in the medical field, in particular, problems of data availability and privacy are hampering research progress and, thus,...
Automated brain tumor classification from MRI scans requires optimized CNN architectures deployable on embedded FPGA platforms. This paper presents an integrated approach combining the Multi-Chaotic Enhanced HEOA (MC-HEOA) for automatic CNN architect...
Background: Positron emission tomography (PET) provides physiologic information central to oncologic staging and treatment assessment, but its availability is limited by cost, radiation exposure, and scanner access. Synthesizing PET from computed tom...
This work investigates the extension of StyleGAN2-ADA to three-dimensional prostate T2-weighted (T2W) MRI generation. The architecture is adapted to operate on 3D anisotropic volumes, enabling stable training at a clinically relevant resolution of
Background: Medical imaging datasets often suffer from class imbalance and limited availability of pathology-rich cases, which limit the performance of machine learning models for segmentation, classification, and vision–language tasks. We prop...
Deep learning models and computer vision are commonly integrated for e-health self-help diagnosis. The abnormal colors and traits of feces can reveal the risks of cancer and digestive diseases. As such, this paper develops a self-help diagnostic syst...
Deep learning for Alzheimer’s disease (AD) detection from structural magnetic resonance imaging (MRI) needs large, labelled datasets, yet many cohorts hold only a few hundred participants, for which conventional augmentation adds little anatomi...
Early detection of colorectal cancer is of paramount importance. Polyp segmentation using deep learning models can play an important role in this process; however, these models require large annotated datasets that are difficult to obtain in medical...
Generative adversarial networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they have been trained to replicate. One recurrent theme in medical imaging, is whether GANs ca...
The rapid development of Generative Adversarial Networks (GANs) has transformed medical image processing, enabling realistic image synthesis, augmentation, and restoration. This study presents a comparative evaluation of three representative GAN arch...
29 August 2024
Background: Machine learning models can provide quick and reliable assessments in place of medical practitioners. With over 50 million adults in the United States suffering from osteoarthritis, there is a need for models capable of interpreting muscu...
Multi-contrast MRI images use different echo and repetition times to highlight different tissues. However, not all desired image contrasts may be available due to scan-time limitations, suboptimal signal-to-noise ratio, and/or image artifacts. Deep l...
This study tackles the challenge of data scarcity in medical AI, focusing on Non-Small-Cell Lung Cancer (NSCLC) diagnosis from Positron Emission Tomography (PET) and Computed Tomography (CT) images. We introduce MedScanGAN, a conditional Generative A...
Background and Objectives: Diffusion models, as a recent advancement in generative modeling, have become central to high-resolution image synthesis and reconstruction. Their rapid progress has notably shaped computer vision and health informatics, pa...
Generative models are used as an alternative data augmentation technique to alleviate the data scarcity problem faced in the medical imaging field. Diffusion models have gathered special attention due to their innovative generation approach, the high...
The role of machine learning (a part of artificial intelligence—AI) in the diagnosis and treatment of various types of oncology is steadily increasing. It is expected that the use of AI in oncology will speed up both diagnostic and treatment pl...
In recent years, the integration of advanced methods in medical imaging has become a major topic of interest due to its potential to enhance diagnostic accuracy, improve clinical efficiency, and increase specialists’ confidence in Artificial In...
While two-view mammography taking both mediolateral-oblique (MLO) and cranio-caudual (CC) views is the current standard method of examination in breast cancer screening, single-view mammography is still being performed in some countries on women of s...
Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved stat...
AI-generated synthetic media are evolving from tools of digital manipulation into a practical antidote for persistent challenges in digital health implementation. However, the computational capabilities that characterise these technologies, their app...
Magnetic particle imaging (MPI) is a promising medical imaging technique producing quantitative images of the distribution of tracer materials (superparamagnetic nanoparticles) without interference from the anatomical background of the imaging object...
(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning...
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