Pathology

Pilot Report for Intracranial Hemorrhage Detection with Deep Learning Implanted Head Computed Tomography Images at Emergency Department.

Hemorrhagic stroke is a serious clinical condition that requires timely diagnosis. An artificial intelligence algorithm system called DeepCT can identify hemorrhagic lesions rapidly from non-contrast head computed tomography (NCCT) images and has received regulatory clearance. A non-controlled retrospective pilot clinical trial was conducted. Patients who received NCCT at the emergency department ...

Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy.

Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior to the lateral resolution. To address this problem, we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volumetric fluorescence microscopy. In contrast to the existing deep learning approaches that req...

Deep learning features encode interpretable morphologies within histological images.

Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotype...

Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images.

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim's blood cells and k...

VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images.

This research work aims to implement an automated segmentation process to extract the endoplasmic reticulum (ER) network in fluorescence microscopy im...

Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm.

Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement...

A Long Short-Term Memory Network for Plasma Diagnosis from Langmuir Probe Data.

Electrostatic probe diagnosis is the main method of plasma diagnosis. However, the traditional diagnosis theory is affected by many factors, and it is...

Identifying pathological slices of gastric cancer via deep learning.

BACKGROUND: The accuracy of histopathology diagnosis largely depends on the pathologist's experience. It usually takes over 10 years to cultivate a se...

A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images.

A clinically comparable Convolutional Neural Network framework-based technique for performing automated classification of cancer grades and tissue str...

Systematic review of artificial intelligence-based image diagnosis for inflammatory bowel disease.

OBJECTIVES: Diagnosis of inflammatory bowel diseases (IBD) involves combining clinical, laboratory, endoscopic, histologic, and radiographic data. Art...

Diagnostic accuracy of fluorescence flow-cytometry technology using Sysmex XN-31 for imported malaria in a non-endemic setting.

Malaria diagnosis based on microscopy is impaired by the gradual disappearance of experienced microscopists in non-endemic areas. Aside from the conve...

Hyperspectral Image Analysis of Colon Tissue and Deep Learning for Characterization of Health care.

Colon cancer is a disease characterized by the unusual and uncontrolled development of cells that are found in the large intestine. If the tumour exte...

Application of Imaging Examination Based on Deep Learning in the Diagnosis of Viral Senile Pneumonia.

Medical image classification technology, preferably which is based on the deep learning, is not only a key auxiliary diagnosis and treatment method in...

Open-source personal pipetting robots with live-cell incubation and microscopy compatibility.

Liquid handling robots have the potential to automate many procedures in life sciences. However, they are not in widespread use in academic settings, ...

Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases.

Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading c...

Weakly supervised segmentation on neural compressed histopathology with self-equivariant regularization.

In digital pathology, segmentation is a fundamental task for the diagnosis and treatment of diseases. Existing fully supervised methods often require ...

Predicting and understanding arterial elasticity from key microstructural features by bidirectional deep learning.

Microstructural features and mechanical properties are closely related in all soft biological tissues. Both yet exhibit considerable inter-individual ...

Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels.

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance w...

Deep learning for blind structured illumination microscopy.

Blind-structured illumination microscopy (blind-SIM) enhances the optical resolution without the requirement of nonlinear effects or pre-defined illum...

Precise measurement of nanoscopic septin ring structures with deep learning-assisted quantitative superresolution microscopy.

The combination of image analysis and superresolution microscopy methods allows for unprecedented insight into the organization of macromolecular asse...