AIMC Topic: Workflow

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Reducing annotation effort in digital pathology: A Co-Representation learning framework for classification tasks.

Medical image analysis
Classification of digital pathology images is imperative in cancer diagnosis and prognosis. Recent advancements in deep learning and computer vision have greatly benefited the pathology workflow by developing automated solutions for classification ta...

CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.

EBioMedicine
BACKGROUND: Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site of origin cannot be found despite a standard diagnostic workup. Because knowledge of a patient's pr...

Blockchain in Healthcare: Insights on COVID-19.

International journal of environmental research and public health
The SARS-CoV2 pandemic has impacted risk management globally. Blockchain has been increasingly applied to healthcare management, as a strategic tool to strengthen operative protocols and to create the proper basis for an efficient and effective evide...

Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network.

European radiology
OBJECTIVES: The goal of the present study was to classify the most common types of plain radiographs using a neural network and to validate the network's performance on internal and external data. Such a network could help improve various radiologica...

Applications of artificial intelligence (AI) in diagnostic radiology: a technography study.

European radiology
OBJECTIVES: Why is there a major gap between the promises of AI and its applications in the domain of diagnostic radiology? To answer this question, we systematically review and critically analyze the AI applications in the radiology domain.

Artificial intelligence in cardiac radiology.

La Radiologia medica
Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role ...

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretati...

Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns.

Open biology
Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionall...

Early experience utilizing artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model.

Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences
BACKGROUND: Recently approved artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams....

Data integration by fuzzy similarity-based hierarchical clustering.

BMC bioinformatics
BACKGROUND: High throughput methods, in biological and biomedical fields, acquire a large number of molecular parameters or omics data by a single experiment. Combining these omics data can significantly increase the capability for recovering fine-tu...