Pathology

Ultrasound Localization Microscopy Using Deep Neural Network.

Noninvasive imaging of microvascular structures in deep tissues provides morphological and functional information for clinical diagnosis and monitoring. Ultrasound localization microscopy (ULM) is an emerging imaging technique that can generate microvascular structures with subwavelength diffraction resolution. However, the clinical utility of ULM is hindered by technical limitations, such as long...

Metric Learning in Histopathological Image Classification: Opening the Black Box.

The application of machine learning techniques to histopathology images enables advances in the field, providing valuable tools that can speed up and facilitate the diagnosis process. The classification of these images is a relevant aid for physicians who have to process a large number of images in long and repetitive tasks. This work proposes the adoption of metric learning that, beyond the task ...

Deep learning enables fast, gentle STED microscopy.

STED microscopy is widely used to image subcellular structures with super-resolution. Here, we report that restoring STED images with deep learning ca...

Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning.

BACKGROUND: In diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BA...

Fourier ptychographic and deep learning using breast cancer histopathological image classification.

Automated, as well as accurate classification with breast cancer histological images, was crucial for medical applications because of detecting malign...

Gigapixel end-to-end training using streaming and attention.

Current hardware limitations make it impossible to train convolutional neural networks on gigapixel image inputs directly. Recent developments in weak...

MIA is an open-source standalone deep learning application for microscopic image analysis.

In recent years, the amount of data generated by imaging techniques has grown rapidly, along with increasing computational power and the development o...

Performance comparison between multi-center histopathology datasets of a weakly-supervised deep learning model for pancreatic ductal adenocarcinoma detection.

BACKGROUND: Pancreatic ductal carcinoma patients have a really poor prognosis given its difficult early detection and the lack of early symptoms. Digi...

Quantitative phase imaging of living red blood cells combining digital holographic microscopy and deep learning.

Digital holographic microscopy as a non-contacting, non-invasive, and highly accurate measurement technology, is becoming a valuable method for quanti...

Using a deep learning neural network for the identification of malignant cells in effusion cytology material.

AIM: To evaluate the application of an artificial neural network in the detection of malignant cells in effusion samples.

Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information.

BACKGROUND: Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is chal...

Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology.

Malignant melanoma (MM) is the "great mime" of dermatopathology, and it can present such rare variants that even the most experienced pathologist migh...

Development of a portable device to quantify hepatic steatosis in potential donor livers.

An accurate estimation of liver fat content is necessary to predict how a donated liver will function after transplantation. Currently, a pathologist ...

Evaluation of reproducible cryogel preparation based on automated image analysis using deep learning.

Cryogels represent a class of porous sponge-like materials possessing unique properties including high-fidelity reproduction of tissue structure and m...

Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning.

A plaque assay-the gold-standard method for measuring the concentration of replication-competent lytic virions-requires staining and usually more than...

A large expert-curated cryo-EM image dataset for machine learning protein particle picking.

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-prote...

Drug synergy model for malignant diseases using deep learning.

Drug synergy has emerged as a viable treatment option for malignancy. Drug synergy reduces toxicity, improves therapeutic efficacy, and overcomes drug...

Implementation of totally robotic right hemicolectomy: lessons learned from a prospective cohort.

Robotics facilitates the realization of intra-corporeal anastomosis during right hemicolectomy and allows extracting the operative specimen through a ...

Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The pu...

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) c...