AIMC Topic: Deep Learning

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Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy.

Renal failure
OBJECTIVES: Renal biopsy is the gold standard for the diagnosis of glomerular diseases including membranous nephropathy (MN), however, it faces challenges in accuracy, objectivity, and reproducibility of tissue evaluation. This study aims to develop ...

Characterizing and differentiating brain states through a CS-KBRs framework for highlighting the synergy of common and specific brain regions.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In the field of neuroscience, understanding the coordination of different brain regions to drive various brain states is critical for revealing the nature of cognitive processes and their manifestation in brain functions and disorders. Despite the pr...

Multimodal Deep Learning Model Based on Ultrasound and Cytological Images Predicts Risk Stratification of cN0 Papillary Thyroid Carcinoma.

Academic radiology
BACKGROUND: Accurately assessing the risk stratification of cN0 papillary thyroid carcinoma (PTC) preoperatively aids in making treatment decisions. We integrated preoperative ultrasound and cytological images of patients to develop and validate a mu...

Deep siamese residual support vector machine with applications to disease prediction.

Computers in biology and medicine
Support Vector Machines (SVMs) excel in classification and regression tasks involving high-dimensional nonlinear data, boasting high accuracy, strong generalization ability, and robust performance. Particularly noteworthy is their outstanding perform...

Mortality and antibiotic timing in deep learning-derived surviving sepsis campaign risk groups: a multicenter study.

Critical care (London, England)
BACKGROUND: The current Surviving Sepsis Campaign (SSC) guidelines provide recommendations on timing of administering antibiotics in sepsis patients based on probability of sepsis and presence of shock. However, there have been minimal efforts to str...

AI-driven smart agriculture using hybrid transformer-CNN for real time disease detection in sustainable farming.

Scientific reports
Plant diseases pose a significant threat to global food security, with severe implications for agricultural productivity. Early and accurate detection of these diseases is crucial, yet it remains a challenging task, significantly impacting crop yield...

Uncertainty aware domain incremental learning for cross domain depression detection.

Scientific reports
Deep learning techniques have demonstrated significant promise for detecting Major Depressive Disorder (MDD) from textual data but they still face limitations in real-world scenarios. Specifically, given the limited data availability, some efforts ha...

A novel framework integrating GeoAI and human perceptions to estimate walkability in Wuhan, China.

Scientific reports
Evidence shows enhanced walking environment promotes overall physical activities and further alleviates the risk of chronic diseases and mental disorders. Current walkability research is limited by traditional GIS methods that fail to capture micro-l...

Generative AI enables medical image segmentation in ultra low-data regimes.

Nature communications
Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. While deep learning automates this task effectively, it struggles in ultra low-data regimes for the scarcity of annotated segmentation m...

Finger drawing on smartphone screens enables early Parkinson's disease detection through hybrid 1D-CNN and BiGRU deep learning architecture.

PloS one
BACKGROUND: Parkinson's disease (PD), a progressive neurodegenerative disorder prevalent in aging populations, manifests clinically through characteristic motor impairments including bradykinesia, rigidity, and resting tremor. Early detection and tim...