AIMC Topic: Deep Learning

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Exploring Contrastive Pre-Training for Domain Connections in Medical Image Segmentation.

IEEE transactions on medical imaging
Unsupervised domain adaptation (UDA) in medical image segmentation aims to improve the generalization of deep models by alleviating domain gaps caused by inconsistency across equipment, imaging protocols, and patient conditions. However, existing UDA...

PASS: Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation.

IEEE transactions on medical imaging
Test-time adaptation (TTA) has emerged as a promising paradigm to handle the domain shifts at test time for medical images from different institutions without using extra training data. However, existing TTA solutions for segmentation tasks suffer fr...

Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species.

PloS one
Sandflies are vectors for several tropical diseases such as leishmaniasis, bartonellosis, and sandfly fever. Moreover, sandflies exhibit species-specificity in transmitting particular pathogen species, with females being responsible for disease trans...

Advancing enterprise risk management with deep learning: A predictive approach using the XGBoost-CNN-BiLSTM model.

PloS one
Enterprise risk management is a key element to ensure the sustainable and steady development of enterprises. However, traditional risk management methods have certain limitations when facing complex market environments and diverse risk events. This s...

Adaptive Dual-Axis Style-Based Recalibration Network With Class-Wise Statistics Loss for Imbalanced Medical Image Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Salient and small lesions (e.g., microaneurysms on fundus) both play significant roles in real-world disease diagnosis under medical image examinations. Although deep neural networks (DNNs) have achieved promising medical image classification perform...

What is the doggest dog? Examination of typicality perception in ImageNet-trained networks.

Neural networks : the official journal of the International Neural Network Society
Due to the emergence of numerous model architectures in recent years, researchers finally have access to models that are diverse enough to properly study them from the perspective of cognitive psychology theories, e.g. Prototype Theory. The theory as...

Source-free time series domain adaptation with wavelet-based multi-scale temporal imputation.

Neural networks : the official journal of the International Neural Network Society
Recent works on source-free domain adaptation (SFDA) for time series reveal the effectiveness of learning domain-invariant temporal dynamics on improving the cross-domain performance of the model. However, existing SFDA methods for time series mainly...

Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery.

Molecular diversity
Farnesoid X receptor (FXR) is a key regulator of bile acid, lipid, and glucose homeostasis, making it a promising target for treating metabolic diseases. FXR antagonists have shown therapeutic potential in cholestasis, metabolic disorders, and certai...

Integrative Multi-Omics and Routine Blood Analysis Using Deep Learning: Cost-Effective Early Prediction of Chronic Disease Risks.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Chronic noncommunicable diseases (NCDS) are often characterized by gradual onset and slow progression, but the difficulty in early prediction remains a substantial health challenge worldwide. This study aims to explore the interconnectedness of disea...

Automated segmentation of the dorsal root ganglia in MRI.

NeuroImage
The dorsal root ganglion (DRG) contains all primary sensory neurons, but its functional role in somatosensory and pain processing remains unclear. Recently, MR imaging techniques have been developed for objective in vivo observation of the DRG. In pa...