Pathology

Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder.

Discrimination of ovarian tumors is necessary for proper treatment. In this study, we developed a convolutional neural network model with a convolutional autoencoder (CNN-CAE) to classify ovarian tumors. A total of 1613 ultrasound images of ovaries with known pathological diagnoses were pre-processed and augmented for deep learning analysis. We designed a CNN-CAE model that removes the unnecessary...

Fast and scalable search of whole-slide images via self-supervised deep learning.

The adoption of digital pathology has enabled the curation of large repositories of gigapixel whole-slide images (WSIs). Computationally identifying WSIs with similar morphologic features within large repositories without requiring supervised training can have significant applications. However, the retrieval speeds of algorithms for searching similar WSIs often scale with the repository size, whic...

Identification of micro- and nanoplastics released from medical masks using hyperspectral imaging and deep learning.

Apart from other severe consequences, the COVID-19 pandemic has inflicted a surge in personal protective equipment usage, some of which, such as medic...

Automatic identification of benign pigmented skin lesions from clinical images using deep convolutional neural network.

OBJECTIVE: We aimed to develop a computer-aided detection (CAD) system for accurate identification of benign pigmented skin lesions (PSLs) from images...

Classification of Multiclass Histopathological Breast Images Using Residual Deep Learning.

Pathologists need a lot of clinical experience and time to do the histopathological investigation. AI may play a significant role in supporting pathol...

MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning.

Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathog...

Artificial intelligence in hematological diagnostics: Game changer or gadget?

The future of clinical diagnosis and treatment of hematologic diseases will inevitably involve the integration of artificial intelligence (AI)-based s...

A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images.

Sickle Cell Anemia (SCA) is a disorder in Red Blood Cells (RBCs) of human blood. Children under five years and pregnant women are mostly affected by S...

Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes.

The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural ...

Robot-assisted endoscopic removal of a huge tricuspid valve myxoma: case report.

BACKGROUND: Cardiac myxoma is the most common benign cardiac tumor. Its tremendous size and fragile character severely bother the surgeons. Several mi...

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and ...

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides.

To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisio...

Premexotac: Machine learning bitterants predictor for advancing pharmaceutical development.

Bitter taste receptors were recently found to be involved in numerous physiological and pathological conditions other than taste and are suggested as ...

A model-based constrained deep learning clustering approach for spatially resolved single-cell data.

Spatially resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile gene expression patterns in tissue context. ...

A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing.

Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental s...

Predictive Medicine for Salivary Gland Tumours Identification Through Deep Learning.

Nowadays, predictive medicine begins to become a reality thanks to Artificial Intelligence (AI) which allows, through the processing of huge amounts o...

A Deep Learning Method for Breast Cancer Classification in the Pathology Images.

Breast cancer is the most common female cancer in the world, and it poses a huge threat to women's health. There is currently promising research conce...

Deep learning-based automated quantification of goblet cell mucus using histological images as a predictor of clinical relapse of ulcerative colitis with endoscopic remission.

BACKGROUND: Mucin depletion is one of the histological indicators of clinical relapse among patients with ulcerative colitis (UC). Mucin depletion is ...

CardioVinci: building blocks for virtual cardiac cells using deep learning.

Advances in electron microscopy (EM) such as electron tomography and focused ion-beam scanning electron microscopy provide unprecedented, three-dimens...

Prediction of Epstein-Barr Virus Status in Gastric Cancer Biopsy Specimens Using a Deep Learning Algorithm.

IMPORTANCE: Epstein-Barr virus (EBV)-associated gastric cancer (EBV-GC) is 1 of 4 molecular subtypes of GC and is confirmed by an expensive molecular ...