Pathology

A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet.

PURPOSE: To improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the ResNet50 algorithm to develop a decision support system.

Validation of a deep learning-based image analysis system to diagnose subclinical endometritis in dairy cows.

The assessment of polymorphonuclear leukocyte (PMN) proportions (%) of endometrial samples is the hallmark for subclinical endometritis (SCE) diagnosis. Yet, a non-biased, automated diagnostic method for assessing PMN% in endometrial cytology slides has not been validated so far. We aimed to validate a computer vision software based on deep machine learning to quantify the PMN% in endometrial cyto...

Explainable artificial intelligence (XAI): closing the gap between image analysis and navigation in complex invasive diagnostic procedures.

LITERATURE REVIEW: Cystoscopy is the gold standard for initial macroscopic assessments of the human urinary bladder to rule out (or diagnose) bladder ...

Frameless robot-assisted stereotactic biopsies for lesions of the brainstem-a series of 103 consecutive biopsies.

PURPOSE: Targeted treatment for brainstem lesions requires above all a precise histopathological and molecular diagnosis. In the current technological...

Disease-Specific Imaging Utilizing Support Vector Machine Classification of H-Scan Parameters: Assessment of Steatosis in a Rat Model.

In medical imaging, quantitative measurements have shown promise in identifying diseases by classifying normal versus pathological parameters from tis...

Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach.

Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. Breast cancer detection needs accurate mammography inte...

Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps.

OBJECTIVES: To investigate the differentiation of premalignant from benign colorectal polyps detected by CT colonography using deep learning.

Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy.

OBJECTIVE: Digital microscopy is used to monitor particulates such as protein aggregates within biopharmaceutical products. The images that result enc...

Deep Learning for Detecting Pneumothorax on Chest Radiographs after Needle Biopsy: Clinical Implementation.

Background Accurate detection of pneumothorax on chest radiographs, the most common complication of percutaneous transthoracic needle biopsies (PTNBs)...

A deep learning model for breast ductal carcinoma in situ classification in whole slide images.

The pathological differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of pivotal importance fo...

Automatic construction of coronary artery tree structure based on vessel blood flow tracking.

We sought to propose an innovative vessel blood flow tracking (VBFT) method to extract coronary artery tree (CAT) and to assess the effectiveness of t...

High-accuracy, direct aberration determination using self-attention-armed deep convolutional neural networks.

Optical microscopes have long been essential for many scientific disciplines. However, the resolution and contrast of such microscopic images are dram...

Malignancy risk stratification of cystic renal lesions based on a contrast-enhanced CT-based machine learning model and a clinical decision algorithm.

OBJECTIVE: To distinguish benign from malignant cystic renal lesions (CRL) using a contrast-enhanced CT-based radiomics model and a clinical decision ...

Sub-micro scale cell segmentation using deep learning.

Automated cell segmentation is key for rapid and accurate investigation of cell responses. As instrumentation resolving power increases, clear delinea...

Multi-channel convolutional neural network architectures for thyroid cancer detection.

Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid speci...

Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation.

In contrast to the well characterized mitotic machinery in eukaryotes it seems as if there is no universal mechanism organizing chromosome segregation...

Quantitative Assessment of Epithelial Proliferation in Rat Mammary Gland Using Artificial Intelligence Independent of Choice of Proliferation Marker.

Epithelial proliferation in the rat mammary gland is recommended in regulatory guidelines as an endpoint for assessment of the in vivo carcinogenic po...

A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor.

A supervised CNN Deep net classifier is proposed for the detection, classification and diagnosis of meningioma brain tumor using deep learning approac...

LeuFeatx: Deep learning-based feature extractor for the diagnosis of acute leukemia from microscopic images of peripheral blood smear.

The abnormal growth of leukocytes causes hematologic malignancies such as leukemia. The clinical assessment methods for the diagnosis of the disease a...

Label-free imaging flow cytometry for analysis and sorting of enzymatically dissociated tissues.

Biomedical research relies on identification and isolation of specific cell types using molecular biomarkers and sorting methods such as fluorescence ...