AIMC Topic: Tomography, X-Ray Computed
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Less is More: Selective reduction of CT data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification performance.
Computers in biology and medicine
Oct 9, 2024
BACKGROUND: Self-supervised pre-training of deep learning models with contrastive learning is a widely used technique in image analysis. Current findings indicate a strong potential for contrastive pre-training on medical images. However, further res...
Development of HepatIA: A computed tomography annotation platform and database for artificial intelligence training in hepatocellular carcinoma detection at a Brazilian tertiary teaching hospital.
Clinics (Sao Paulo, Brazil)
Oct 9, 2024
BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent tumor with high mortality rates. Computed tomography (CT) is crucial in the non-invasive diagnosis of HCC. Recent advancements in artificial intelligence (AI) have shown significant potential ...
Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.
PloS one
Oct 9, 2024
The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transpare...
Evaluation of the accuracy of automated segmentation based on deep learning for prostate cancer patients.
Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Oct 8, 2024
PURPOSE: This study evaluated the accuracy of a commercial deep learning (DL)-based algorithm for segmenting the prostate, seminal vesicles (SV), and organs at risk (OAR) in patients with prostate cancer.
Artificial intelligence for detection and characterization of focal hepatic lesions: a review.
Abdominal radiology (New York)
Oct 5, 2024
Focal liver lesions (FLL) are common incidental findings in abdominal imaging. While the majority of FLLs are benign and asymptomatic, some can be malignant or pre-malignant, and need accurate detection and classification. Current imaging techniques,...
Machine learning based classification of spontaneous intracranial hemorrhages using radiomics features.
Neuroradiology
Oct 5, 2024
PURPOSE: To assess the efficacy of radiomics features extracted from non-contrast computed tomography (NCCT) scans in differentiating multiple etiologies of spontaneous intracerebral hemorrhage (ICH).
Computed Tomography-Based Radiomics with Machine Learning Outperforms Radiologist Assessment in Estimating Colorectal Liver Metastases Pathologic Response After Chemotherapy.
Annals of surgical oncology
Oct 5, 2024
OBJECTIVES: This study was designed to assess computed tomography (CT)-based radiomics of colorectal liver metastases (CRLM), extracted from posttreatment scans in estimating pathologic treatment response to neoadjuvant therapy, and to compare treatm...
Neoadjuvant Therapy
Follow-Up Studies
Radiologists
Response Evaluation Criteria in Solid Tumors
Middle Aged
Liver Neoplasms
Antineoplastic Combined Chemotherapy Protocols
Retrospective Studies
Tomography, X-Ray Computed
Female
Aged
Adult
Colorectal Neoplasms
Radiomics
Male
Humans
Machine Learning
Prognosis
Clinical Impact of Radiologist's Alert System on Patient Care for High-risk Incidental CT Findings: A Machine Learning-Based Risk Factor Analysis.
Academic radiology
Oct 4, 2024
RATIONALE AND OBJECTIVES: Efficient communication between radiologists and clinicians ordering computed tomography (CT) examinations is crucial for managing high-risk incidental CT findings (ICTFs). Herein, we introduced a Radiologist's Alert and Pat...
Automated orofacial virtual patient creation: A proof of concept.
Journal of dentistry
Oct 2, 2024
OBJECTIVES: To (1) construct a virtual patient (VP) using facial scan, intraoral scan, and low-dose computed tomography scab based on an Artificial intelligence (AI)-approach, (2) quantitatively compare it with AI-refined and semi-automatic registrat...
Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans.
Nature biomedical engineering
Oct 1, 2024
The application of machine learning to tasks involving volumetric biomedical imaging is constrained by the limited availability of annotated datasets of three-dimensional (3D) scans for model training. Here we report a deep-learning model pre-trained...
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