Pathology

PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease.

Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the PArkinson disease Denoising and Segmentation Network (PADS-Net), to simultaneously denoise and segment transcranial ultrasound images of midbrain for accurate PD diagnosis. The PADS-Net is built upon generative adversarial networks and incorporates a multi...

Discrimination of Benign and Malignant Thyroid Nodules through Comparative Analyses of Human Saliva Samples via Metabolomics and Deep-Learning-Guided Label-free SERS.

Thyroid nodules are a very common entity. The overall prevalence in the populace is estimated to be around 65-68%, among which a small portion (less than 5%) is malignant (cancerous). Therefore, it is important to discriminate benign thyroid nodules from malignant thyroid nodules. In this study, an equal number of participants with benign and malignant thyroid nodules ( = 10/group) were recruited....

Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning.

Gram staining has been a frequently used staining protocol in microbiology. It is vulnerable to staining artifacts due to, e.g., operator errors and c...

Boosting skin cancer diagnosis accuracy with ensemble approach.

Skin cancer is common and deadly, hence a correct diagnosis at an early age is essential. Effective therapy depends on precise classification of the s...

Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

PROBLEM: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer ...

ULM-MbCNRT: In Vivo Ultrafast Ultrasound Localization Microscopy by Combining Multibranch CNN and Recursive Transformer.

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit by localizing tiny microbubbles (MBs), thus enabling the microvascul...

Deep Learning in Ultrasound Localization Microscopy: Applications and Perspectives.

Ultrasound localization microscopy (ULM) is a novel super-resolution imaging technique that can image the vasculature in vivo at depth with resolution...

VoxelMorph-Based Deep Learning Motion Correction for Ultrasound Localization Microscopy of Spinal Cord.

Accurate assessment of spinal cord vasculature is important for the urgent diagnosis of injury and subsequent treatment. Ultrasound localization micro...

Deep Power-Aware Tunable Weighting for Ultrasound Microvascular Imaging.

Ultrasound microvascular imaging (UMI), including ultrafast power Doppler imaging (uPDI) and ultrasound localization microscopy (ULM), obtains blood f...

Graph neural networks in histopathology: Emerging trends and future directions.

Histopathological analysis of whole slide images (WSIs) has seen a surge in the utilization of deep learning methods, particularly Convolutional Neura...

Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments.

Industry 4.0 represents the fourth industrial revolution, which is characterized by the incorporation of digital technologies, the Internet of Things ...

Partial-Label Contrastive Representation Learning for Fine-Grained Biomarkers Prediction From Histopathology Whole Slide Images.

In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSIs) face chall...

Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning.

Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach utilizing unlabeled data. The Jigsaw puzzle task ...

MSRMMP: Multi-scale residual module and multi-layer pseudo-supervision for weakly supervised segmentation of histopathological images.

Accurate semantic segmentation of histopathological images plays a crucial role in accurate cancer diagnosis. While fully supervised learning models h...

Evaluating the pathological and clinical implications of errors made by an artificial intelligence colon biopsy screening tool.

OBJECTIVE: Artificial intelligence (AI) tools for histological diagnosis offer great potential to healthcare, yet failure to understand their clinical...

Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model.

Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression l...

Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features.

BACKGROUND: First-line treatment for advanced gastric adenocarcinoma (GAC) with human epidermal growth factor receptor 2 (HER2) is trastuzumab combine...

Insights into AI advances in immunohistochemistry for effective breast cancer treatment: a literature review of ER, PR, and HER2 scoring.

Breast cancer is a significant health challenge, with accurate and timely diagnosis being critical to effective treatment. Immunohistochemistry (IHC) ...

Automated ultrasonography of hepatocellular carcinoma using discrete wavelet transform based deep-learning neural network.

This study introduces HCC-Net, a novel wavelet-based approach for the accurate diagnosis of hepatocellular carcinoma (HCC) from abdominal ultrasound (...

Integrating artificial intelligence with endoscopic ultrasound in the early detection of bilio-pancreatic lesions: Current advances and future prospects.

The integration of Artificial Intelligence (AI) in endoscopic ultrasound (EUS) represents a transformative advancement in the early detection and mana...