Pathology

Monopolar, bipolar, tripolar, and tetrapolar configurations in robot assisted electrical impedance sensing.

Tissue recognition is a critical process during a Robot-assisted minimally invasive surgery (RMIS) and it relies on the involvement of advanced sensing technology.In this paper, the concept of Robot Assisted Electrical Impedance Sensing (RAEIS) is utilized and further developed aiming to sense the electrical bioimpedance of target tissue directly based on the existing robotic instruments and contr...

A Deep-Learning Based System for Rapid Genus Identification of Pathogens under Hyperspectral Microscopic Images.

Infectious diseases have always been a major threat to the survival of humanity. Additionally, they bring an enormous economic burden to society. The conventional methods for bacteria identification are expensive, time-consuming and laborious. Therefore, it is of great importance to automatically rapidly identify pathogenic bacteria in a short time. Here, we constructed an AI-assisted system for a...

Comparative analysis of high- and low-level deep learning approaches in microsatellite instability prediction.

Deep learning-based approaches in histopathology can be largely divided into two categories: a high-level approach using an end-to-end model and a low...

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network.

Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate can...

Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm.

Relying on expert diagnosis, it solves the problem of fan failure efficiency and meets the needs of automatic inspection and intelligent operation mon...

Machine learning-enabled quantitative ultrasound techniques for tissue differentiation.

PURPOSE: Quantitative ultrasound (QUS) infers properties about tissue microstructure from backscattered radio-frequency ultrasound data. This paper de...

A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven p Predictions in Proteins.

Existing computational methods for estimating p values in proteins rely on theoretical approximations and lengthy computations. In this work, we use a...

Deep learning with multiresolution handcrafted features for brain MRI segmentation.

The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo computed tomography (CT) images which are used to achieve pos...

Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly.

Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. Howeve...

Development and clinical applicability of MRI-based 3D prostate models in the planning of nerve-sparing robot-assisted radical prostatectomy.

The interpretation of conventional MRI may be limited by the two-dimensional presentation of the images. To develop patient-specific MRI prostate-base...

Feasibility study of three-material decomposition in dual-energy cone-beam CT imaging with deep learning.

In this work, a dedicated end-to-end deep convolutional neural network, named as Triple-CBCT, is proposed to demonstrate the feasibility of reconstruc...

MRI-based and robot-assisted stereotactic biopsy with intraoperative CT imaging.

BACKGROUND: As a pioneer center in the field of stereotaxy, Sainte-Anne school has always advocated the use of intraoperative imaging for stereotactic...

Machine Learning Analysis Provides Insight into Mechanisms of Protein Particle Formation Inside Containers During Mechanical Agitation.

Container choice can influence particle generation within protein formulations. Incompatibility between proteins and containers can manifest as increa...

Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules.

Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed...

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.

This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the r...

Application of deep learning methods: From molecular modelling to patient classification.

We are now well into the information driven age with complex, heterogeneous, datasets in the biological sciences continuing to grow at a rapid pace. M...

Validity of at-home rapid antigen lateral flow assay and artificial intelligence read to detect SARS-CoV-2.

BACKGROUND: The gold standard for COVID-19 diagnosis-reverse-transcriptase polymerase chain reaction (RT-PCR)- is expensive and often slow to yield re...

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.

Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient's outcome. Previo...

Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network.

Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learni...

Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma.

Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung a...