Pathology

DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images.

While many pre-defined computed tomographic (CT) measures have been utilized to characterize chronic obstructive pulmonary disease (COPD), it is still challenging to represent pathological alternations of multiple dimensions and highly spatial heterogeneity. Deep CNN transferred multiple instance learning (DCT-MIL) is proposed to identify COPD via CT images. After the lung is divided into eight se...

Magnetic Resonance Guided Navigation of Untethered Microgrippers.

Microsurgical tools offer a path to less invasive clinical procedures with improved access, reduced trauma, and better recovery outcomes. There are a variety of rigid and flexible endoscopic devices that have significantly advanced diagnostics and microsurgery. However, they rely on wires or tethers for guidance and operation of small end-effector tools. While untethered physiologically responsive...

Computational Cytology: Lessons Learned from Pap Test Computer-Assisted Screening.

BACKGROUND: In the face of rapid technological advances in computational cytology including artificial intelligence (AI), optimization of its applicat...

Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology.

PURPOSE: Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, ac...

The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future.

In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients...

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning.

OBJECTIVE: Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underl...

Semantic segmentation to identify bladder layers from H&E Images.

BACKGROUND: Identification of bladder layers is a necessary prerequisite to bladder cancer diagnosis and prognosis. We present a method of multi-class...

Deep learning-based classification of the mouse estrous cycle stages.

There is a rapidly growing demand for female animals in preclinical animal, and thus it is necessary to determine animals' estrous cycle stages from v...

A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.

Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemora...

A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging.

Semisolid magnetization transfer contrast (MTC) and chemical exchange saturation transfer (CEST) MRI based on MT phenomenon have shown potential to ev...

The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images.

Background Deep learning has presented considerable potential and is gaining more importance in computer assisted diagnosis. As the gold standard for ...

MicroRNA Profiling as a Methodology to Diagnose Ménière's Disease: Potential Application of Machine Learning.

OBJECTIVE: Diagnosis and treatment of Ménière's disease remains a significant challenge because of our inability to understand what is occurring on a ...

Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review.

This systematic review analyses and describes the application and diagnostic accuracy of Artificial Intelligence (AI) methods used for detection and g...

Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.

Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients wit...

Classification of glomerular pathological findings using deep learning and nephrologist-AI collective intelligence approach.

BACKGROUND: Automated classification of glomerular pathological findings is potentially beneficial in establishing an efficient and objective diagnosi...

NuClick: A deep learning framework for interactive segmentation of microscopic images.

Object segmentation is an important step in the workflow of computational pathology. Deep learning based models generally require large amount of labe...

Non-ischemic endocardial scar geometric remodeling toward topological machine learning.

Scar tissues have been important factors in determining the progression of myocardial diseases and the development of adverse cardiac failure outcomes...

Computational Technology with Artificial Intelligence and Machine Learning: What Should a Cytologist Do with It?

Identifying Fatal Head Injuries on Postmortem Computed Tomography Using Convolutional Neural Network/Deep Learning: A Feasibility Study.

Postmortem computed tomography (PMCT) is a relatively recent advancement in forensic pathology practice that has been increasingly used as an ancillar...

Fully multi-target segmentation for breast ultrasound image based on fully convolutional network.

Ultrasound image segmentation plays an important role in computer-aided diagnosis of breast cancer. Existing approaches focused on extracting the tumo...