Pathology

Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images-The ACDC@LungHP Challenge 2019.

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) ...

Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model.

BACKGROUND AND PURPOSE: Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a deep learning model for fully automated detection and segmentation of brain metastases in patients with malignant melanoma using clinical routine MR imaging.

Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration.

An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occa...

Aggregation of cohorts for histopathological diagnosis with deep morphological analysis.

There have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typica...

Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning.

One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed. In this st...

Transforming UTE-mDixon MR Abdomen-Pelvis Images Into CT by Jointly Leveraging Prior Knowledge and Partial Supervision.

Computed tomography (CT) provides information for diagnosis, PET attenuation correction (AC), and radiation treatment planning (RTP). Disadvantages of...

Imbalanced Breast Cancer Classification Using Transfer Learning.

Accurate breast cancer detection using automated algorithms remains a problem within the literature. Although a plethora of work has tried to address ...

A Transfer Learning Based Super-Resolution Microscopy for Biopsy Slice Images: The Joint Methods Perspective.

Higher-resolution biopsy slice images reveal many details, which are widely used in medical practice. However, taking high-resolution slice images is ...

The implementation of TORS for head and neck surgery in Thailand.

Transoral robotic surgery (TORS) is a novel surgical treatment of head and neck cancers, mainly for limited tumor in oropharynx and supraglottis. Desp...

Biohybrid robotics: From the nanoscale to the macroscale.

Biohybrid robotics is a field in which biological entities are combined with artificial materials in order to obtain improved performance or features ...

Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study.

BACKGROUND: An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histo...

Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images.

Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of goo...

Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification.

Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor...

Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning.

Cervical cancer is one of the most frequent cancers in women worldwide, yet the early detection and treatment of lesions via regular cervical screenin...

Lung cancer prediction by Deep Learning to identify benign lung nodules.

INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer...

Verifying explainability of a deep learning tissue classifier trained on RNA-seq data.

For complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the pr...

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.

Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, res...

Hg(II) sensing, catalytic, antioxidant, antimicrobial, and anticancer potential of Garcinia mangostana and α-mangostin mediated silver nanoparticles.

This study reports synthesis of Garcinia mangostana fruit pericarp (unwanted waste material) and α-mangostin mediated silver nanoparticles (AgNPs). Th...

A tree-based multiclassification of breast tumor histopathology images through deep learning.

Worldwide, the burden of cancer is drastically increasing over the past few years. Among all types of cancers in women, breast cancer (BrC) is the mai...

Wavelet decomposition facilitates training on small datasets for medical image classification by deep learning.

The adoption of low-dose computed tomography (LDCT) as the standard of care for lung cancer screening results in decreased mortality rates in high-ris...