AI Medical Compendium Journal:
Computer methods and programs in biomedicine

Showing 71 to 80 of 844 articles

Leveraging Transformers-based models and linked data for deep phenotyping in radiology.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Despite significant investments in the normalization and the standardization of Electronic Health Records (EHRs), free text is still the rule rather than the exception in clinical notes. The use of free text has implications...

HistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven predictive integration.

Computer methods and programs in biomedicine
Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the capability for immediate case sharing between hospitals. Recent advances in deep learning-based methods...

A novel lightweight deep learning based approaches for the automatic diagnosis of gastrointestinal disease using image processing and knowledge distillation techniques.

Computer methods and programs in biomedicine
BACKGROUND: Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational deman...

Machine learning-based forecast of Helmet-CPAP therapy failure in Acute Respiratory Distress Syndrome patients.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Helmet-Continuous Positive Airway Pressure (H-CPAP) is a non-invasive respiratory support that is used for the treatment of Acute Respiratory Distress Syndrome (ARDS), a severe medical condition diagnosed when symptoms like ...

Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation.

Computer methods and programs in biomedicine
BACKGROUND: Data sharing in healthcare is vital for advancing research and personalized medicine. However, the process is hindered by privacy, ethical, and legal challenges associated with patient data. Synthetic data generation emerges as a promisin...

Robust multi-modal fusion architecture for medical data with knowledge distillation.

Computer methods and programs in biomedicine
BACKGROUND: The fusion of multi-modal data has been shown to significantly enhance the performance of deep learning models, particularly on medical data. However, missing modalities are common in medical data due to patient specificity, which poses a...

Multimodal autism detection: Deep hybrid model with improved feature level fusion.

Computer methods and programs in biomedicine
OBJECTIVE: Social communication difficulties are a characteristic of autism spectrum disorder (ASD), a neurodevelopmental condition. The earlier method of diagnosing autism largely relied on error-prone behavioral observation of symptoms. More intell...

Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Accurate prediction of perioperative major adverse cardiovascular events (MACEs) is crucial, as it not only aids clinicians in comprehensively assessing patients' surgical risks and tailoring personalized surgical and periop...

Transferable automatic hematological cell classification: Overcoming data limitations with self-supervised learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Classification of peripheral blood and bone marrow cells is critical in the diagnosis and monitoring of hematological disorders. The development of robust and reliable automatic classification systems is hampered by data sca...

MP-FocalUNet: Multiscale parallel focal self-attention U-Net for medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Medical image segmentation has been significantly improved in recent years with the progress of Convolutional Neural Networks (CNNs). Due to the inherent limitations of convolutional operations, CNNs perform poorly in learni...