Pathology

Identify Representative Samples by Conditional Random Field of Cancer Histology Images.

Pathology analysis is crucial to precise cancer diagnoses and the succeeding treatment plan as well. To detect abnormality in histopathology images with prevailing patch-based convolutional neural networks (CNNs), contextual information often serves as a powerful cue. However, as whole-slide images (WSIs) are characterized by intense morphological heterogeneity and extensive tissue scale, a straig...

Efficient framework for brain tumor detection using different deep learning techniques.

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural...

A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is c...

DLA-H: A Deep Learning Accelerator for Histopathologic Image Classification.

It is more than a decade since machine learning and especially its leading subtype deep learning have become one of the most interesting topics in alm...

Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks.

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple st...

Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta.

Digital imaging, including the use of artificial intelligence, has been increasingly applied to investigate the placenta and its related pathology. Ho...

Evaluation of Machine Learning Techniques for Traffic Flow-Based Intrusion Detection.

Cybersecurity is one of the great challenges of today's world. Rapid technological development has allowed society to prosper and improve the quality ...

Novel approaches utilizing robotic navigational bronchoscopy: a single institution experience.

The effective biopsy of pulmonary nodules is crucial to early diagnosis and consequent effective treatment for patients. As a relatively new procedure...

Digital Histopathological Discrimination of Label-Free Tumoral Tissues by Artificial Intelligence Phase-Imaging Microscopy.

Histopathology is the gold standard for disease diagnosis. The use of digital histology on fresh samples can reduce processing time and potential imag...

Identification of glycolysis genes signature for predicting prognosis in malignant pleural mesothelioma by bioinformatics and machine learning.

BACKGROUND: Glycolysis-related genes as prognostic markers in malignant pleural mesothelioma (MPM) is still unclear. We hope to explore the relationsh...

Artificial intelligence techniques for neuropathological diagnostics and research.

Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neur...

Human induced pluripotent stem cell formation and morphology prediction during reprogramming with time-lapse bright-field microscopy images using deep learning methods.

BACKGROUND AND OBJECTIVE: Human induced pluripotent stem cells (hiPSCs) represent an ideal source for patient specific cell-based regenerative medicin...

ATTransUNet: An enhanced hybrid transformer architecture for ultrasound and histopathology image segmentation.

Recently, researchers have introduced Transformer into medical image segmentation networks to encode long-range dependency, which makes up for the def...

Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.

The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around th...

Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models.

OBJECTIVES: Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic...

Collective computational intelligence in biology - Emergence of memory in somatic tissues.

Role of memory in the function of biological tissues, organs and organisms remains unexplored with many unanswered questions. In this study, the emerg...

A graph neural network framework for mapping histological topology in oral mucosal tissue.

BACKGROUND: Histological feature representation is advantageous for computer aided diagnosis (CAD) and disease classification when using predictive te...

Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network.

BACKGROUND AND OBJECTIVE: Whole slide image (WSI) classification and lesion localization within giga-pixel slide are challenging tasks in computationa...

Development of an automated combined positive score prediction pipeline using artificial intelligence on multiplexed immunofluorescence images.

Immunotherapy targeting immune checkpoint proteins, such as programmed cell death ligand 1 (PD-L1), has shown impressive outcomes in many clinical tri...

Sperm-cell DNA fragmentation prediction using label-free quantitative phase imaging and deep learning.

In intracytoplasmic sperm injection (ICSI), a single sperm cell is selected and injected into an egg. The quality of the chosen sperm and specifically...