AIMC Topic: Deep Learning
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Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.
Breast cancer research : BCR
Feb 27, 2025
OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model based on longitudinal ultrasound data and clinical features to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breas...
Development of an artificial intelligence-based multimodal diagnostic system for early detection of biliary atresia.
BMC medicine
Feb 27, 2025
BACKGROUND: Early diagnosis of biliary atresia (BA) is crucial for improving patient outcomes, yet remains a significant global challenge. This challenge may be ameliorated through the application of artificial intelligence (AI). Despite the promise ...
MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model.
BMC biology
Feb 27, 2025
BACKGROUND: Cyclic peptides, known for their high binding affinity and low toxicity, show potential as innovative drugs for targeting "undruggable" proteins. However, their therapeutic efficacy is often hindered by poor membrane permeability. Over th...
Auxiliary meta-learning strategy for cancer recognition: leveraging external data and optimized feature mapping.
BMC cancer
Feb 27, 2025
As reported by the International Agency for Research on Cancer (IARC), the global incidence of cancer reached nearly 20 million new cases in recent years, with cancer-related fatalities amounting to around 9.7 million. This underscores the profound i...
A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases.
Scientific reports
Feb 27, 2025
Detecting cassava leaf disease is challenging because it is hard to identify diseases accurately through visual inspection. Even trained agricultural experts may struggle to diagnose the disease correctly which leads to potential misjudgements. Tradi...
T1-weighted MRI-based brain tumor classification using hybrid deep learning models.
Scientific reports
Feb 27, 2025
Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as a cornerstone in diagnosing brain health issues, providing essential data for healthcare decisions....
Deeply supervised two stage generative adversarial network for stain normalization.
Scientific reports
Feb 27, 2025
The color variations present in histopathological images pose a significant challenge to computational pathology and, consequently, negatively affect the performance of certain pathological image analysis methods, especially those based on deep learn...
Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models.
Scientific reports
Feb 27, 2025
Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration and cognitive decline. Early diagnosis is challenging due to the complexity of sporadic ALS and the lack of a defined risk population. In this study, we...
Neurofind: using deep learning to make individualised inferences in brain-based disorders.
Translational psychiatry
Feb 27, 2025
Within precision psychiatry, there is a growing interest in normative models given their ability to parse heterogeneity. While they are intuitive and informative, the technical expertise and resources required to develop normative models may not be a...
Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.
European journal of nuclear medicine and molecular imaging
Feb 26, 2025
PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion artifacts, but the shortened scanning time increases statistical noise and may affect diagnostic qu...
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