Pathology

Deep Learning Improves Speed and Accuracy of Prostate Gland Segmentations on Magnetic Resonance Imaging for Targeted Biopsy.

PURPOSE: Targeted biopsy improves prostate cancer diagnosis. Accurate prostate segmentation on magnetic resonance imaging (MRI) is critical for accurate biopsy. Manual gland segmentation is tedious and time-consuming. We sought to develop a deep learning model to rapidly and accurately segment the prostate on MRI and to implement it as part of routine magnetic resonance-ultrasound fusion biopsy in...

Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning.

The study aimed at developing a deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM). Subjects who underwent mandibular advancement surgery were enrolled and divided into a training group and a test group. The DL model was trained using 3D photographs and CBCT data based on s...

Multimodal super-resolved q-space deep learning.

Super-resolvedq-space deep learning (SR-q-DL) has been developed to estimate high-resolution (HR) tissue microstructure maps from low-quality diffusio...

Tumor classification in automated breast ultrasound (ABUS) based on a modified extracting feature network.

People can get consistent Automated Breast Ultrasound (ABUS) images due to the imaging mechanism of scanning. Therefore, it has unique advantages in b...

A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy.

Histopathological diagnosis of pancreatic ductal adenocarcinoma (PDAC) on endoscopic ultrasonography-guided fine-needle biopsy (EUS-FNB) specimens has...

A hybrid deep learning approach for gland segmentation in prostate histopathological images.

BACKGROUND: In digital pathology, the morphology and architecture of prostate glands have been routinely adopted by pathologists to evaluate the prese...

Democratising deep learning for microscopy with ZeroCostDL4Mic.

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthus...

Imaging error compensation method for through-focus scanning optical microscopy images based on deep learning.

Through-focus scanning optical microscopy (TSOM) is a model-based nanoscale metrology technique which combines conventional bright-field microscopy an...

A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images.

The differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung ...

Jaya Ant lion optimization-driven Deep recurrent neural network for cancer classification using gene expression data.

Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early ...

A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks.

Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tis...

Special Issue on Digital Pathology, Tissue Image Analysis, Artificial Intelligence, and Machine Learning: Approximation of the Effect of Novel Technologies on Toxicologic Pathology.

For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology h...

Impact of image compression on deep learning-based mammogram classification.

Image compression is used in several clinical organizations to help address the overhead associated with medical imaging. These methods reduce file si...

Tens of images can suffice to train neural networks for malignant leukocyte detection.

Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires l...

Diagnosis of cholangiocarcinoma from microscopic hyperspectral pathological dataset by deep convolution neural networks.

This paper focuses on automatic Cholangiocarcinoma (CC) diagnosis from microscopic hyperspectral (HSI) pathological dataset with deep learning method....

Generative Deep Learning in Digital Pathology Workflows.

Many modern histopathology laboratories are in the process of digitizing their workflows. Digitization of tissue images has made it feasible to resear...

Deep learning-based molecular morphometrics for kidney biopsies.

Morphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in hum...

CEM500K, a large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning.

Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge. Supervised deep learning (DL) methods that rely on region-of...

A deep learning approach to identifying immunogold particles in electron microscopy images.

Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are t...

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey.

Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and trea...