Pathology

Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits.

Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the fi...

Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation.

BACKGROUND: Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spec...

Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application.

Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of ...

New convolutional neural network model for screening and diagnosis of mammograms.

Breast cancer is the most common cancer in women and poses a great threat to women's life and health. Mammography is an effective method for the diagn...

Graph Neural Network-Based Diagnosis Prediction.

Diagnosis prediction is an important predictive task in health care that aims to predict the patient future diagnosis based on their historical medica...

Classification of cervical neoplasms on colposcopic photography using deep learning.

Colposcopy is widely used to detect cervical cancers, but experienced physicians who are needed for an accurate diagnosis are lacking in developing co...

Improving the accuracy of medical diagnosis with causal machine learning.

Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms ...

An annotated fluorescence image dataset for training nuclear segmentation methods.

Fully-automated nuclear image segmentation is the prerequisite to ensure statistically significant, quantitative analyses of tissue preparations,appli...

Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning.

Frozen sections provide a basis for rapid intraoperative diagnosis that can guide surgery, but the diagnoses often challenge pathologists. Here we pro...

Development of a Deep Learning-Based Model for Diagnosing Breast Nodules With Ultrasound.

OBJECTIVES: Artificial intelligence (AI) has been an important addition to medicine. We aimed to explore the use of deep learning (DL) to distinguish ...

Development and validation of two artificial intelligence models for diagnosing benign, pigmented facial skin lesions.

OBJECTIVE: This study used deep learning for diagnosing common, benign hyperpigmentation.

A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation.

Carcinoma of unknown primary (CUP), defined as metastatic cancers with unknown cancer origin, occurs in 3-5 per 100 cancer patients in the United Stat...

Long-distance disorder-disorder relation extraction with bootstrapped noisy data.

OBJECTIVE: Artificial intelligence in healthcare increasingly relies on relations in knowledge graphs for algorithm development. However, many importa...

The effect of laser on the efficiency of membrane clarification of pomegranate juice.

In this study, a laser node with power 1 W and wavelength 532 nm was used to reduce the membrane fouling and evaluate its effect on the efficiency of ...

Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models.

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early dia...

Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion.

BACKGROUND AND OBJECTIVE: Breast cancer is a frequently diagnosed cancer in women, contributing to significant mortality rates. Death rates are relati...

Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images.

Pathologists are responsible for cancer type diagnoses from histopathological cancer tissues. However, it is known that microscopic examination is ted...

A deep learning model to predict RNA-Seq expression of tumours from whole slide images.

Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of trea...

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides.

Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar la...

Deep adaptive registration of multi-modal prostate images.

Artificial intelligence, especially the deep learning paradigm, has posed a considerable impact on cancer imaging and interpretation. For instance, fu...