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| http://dx.doi.org/10.22037/aaem.v9i1.1122 | DOI Listing |
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Leveraging descriptor learning and functional map-based shape matching for automated anatomical Landmarking in mouse mandibles.
J Anat
January 2025
Center for Development Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, Washington, USA.
Geometric morphometrics is used in the biological sciences to quantify morphological traits. However, the need for manual landmark placement hampers scalability, which is both time-consuming, labor-intensive, and open to human error. The selected landmarks embody a specific hypothesis regarding the critical geometry relevant to the biological question.
View Article and Find Full Text PDFDeep learning methods for improving the accuracy and efficiency of pathological image analysis.
Sci Prog
January 2025
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China.
This study presents a novel integration of two advanced deep learning models, U-Net and EfficientNetV2, to achieve high-precision segmentation and rapid classification of pathological images. A key innovation is the development of a new heatmap generation algorithm, which leverages meticulous image preprocessing, data enhancement strategies, ensemble learning, attention mechanisms, and deep feature fusion techniques. This algorithm not only produces highly accurate and interpretatively rich heatmaps but also significantly improves the accuracy and efficiency of pathological image analysis.
View Article and Find Full Text PDFA Multi-Source drug combination and Omnidirectional feature fusion approach for predicting Drug-Drug interaction events.
J Biomed Inform
January 2025
Northwest Normal University, College of Computer Science and Engineering, Lanzhou, China. Electronic address:
Background: In the medical context where polypharmacy is increasingly common, accurately predicting drug-drug interactions (DDIs) is necessary for enhancing clinical medication safety and personalized treatment. Despite progress in identifying potential DDIs, a deep understanding of the underlying mechanisms of DDIs remains limited, constraining the rapid development and clinical application of new drugs.
Methods: This study introduces a novel multimodal drug-drug interaction (MMDDI) model based on multi-source drug data and comprehensive feature fusion techniques, aiming to improve the accuracy and depth of DDI prediction.
Fulminant non-traumatic Clostridium perfringens necrotizing fasciitis and myonecrosis in a child with acute myeloid leukemia.
J Infect Public Health
December 2024
Department of Pediatric Infectious Diseases, Ministry of National Guard-Health Affairs (MNGHA), King Abdullah Specialist Children Hospital, Riyadh, Saudi Arabia; King Abdullah International Medical Research Centre, King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia; Department of Pathology and Laboratory Medicine, King Abdulaziz Medical City (KAMC), MNGHA, Riyadh, Saudi Arabia.
Necrotizing fasciitis is a potentially life-threatening infection that can lead to rapid muscular and fascial necrosis, often resulting in sepsis. In addition to the rapid disease progression, diagnosing this disease in children can be challenging as they cannot accurately communicate their symptoms. Spontaneous necrotizing fasciitis secondary to Clostridial infection has rarely been described in the literature but occurs in neutropenic patients with significant morbidity and mortality from myonecrosis and gas gangrene.
View Article and Find Full Text PDFAutomated Detection of Filamentous Fungal Keratitis on Whole Slide Images of Potassium Hydroxide Smears with Multiple Instance Learning.
Ophthalmol Sci
November 2024
Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Purpose: The diagnosis of fungal keratitis using potassium hydroxide (KOH) smears of corneal scrapings enables initiation of the correct antimicrobial therapy at the point-of-care but requires time-consuming manual examination and expertise. This study evaluates the efficacy of a deep learning framework, dual stream multiple instance learning (DSMIL), in automating the analysis of whole slide imaging (WSI) of KOH smears for rapid and accurate detection of fungal infections.
Design: Retrospective observational study.
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