Pathology

Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors.

Advances in computerized image analysis and the use of artificial intelligence-based approaches for image-based analysis and construction of prediction algorithms represent a new era for noninvasive biomarker discovery. In recent literature, it has become apparent that radiologic images can serve as mineable databases that contain large amounts of quantitative features with potential clinical sign...

Soft Tissue Sarcoma: Preoperative MRI-Based Radiomics and Machine Learning May Be Accurate Predictors of Histopathologic Grade.

The purpose of this study was to assess the value of radiomics features for differentiating soft tissue sarcomas (STSs) of different histopathologic grades. The T1-weighted and fat-suppressed T2-weighted MR images of 70 STSs of varying grades (35 low-grade [grades 1 and 2], 35 high-grade [grade 3]) formed the primary dataset used to train multiple machine learning algorithms for the construction...

Deep learning in digital pathology image analysis: a survey.

Deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafte...

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning.

Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to ...

Efficient Deep Learning Architecture for Detection and Recognition of Thyroid Nodules.

Ultrasonography is widely used in the clinical diagnosis of thyroid nodules. Ultrasound images of thyroid nodules have different appearances, interior...

Machine learning and statistical analyses for extracting and characterizing "fingerprints" of antibody aggregation at container interfaces from flow microscopy images.

Therapeutic proteins are exposed to numerous stresses during their manufacture, shipping, storage and administration to patients, causing them to aggr...

Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data.

We demonstrate that prostate cancer can be identified by flow cytometric profiling of blood immune cell subsets. Herein, we profiled natural killer (N...

Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.

BACKGROUND: Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. T...

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.

We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and eosin-stained histopathology slide images from 28 c...

Pan-cancer image-based detection of clinically actionable genetic alterations.

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which ...

Revealing architectural order with quantitative label-free imaging and deep learning.

We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of ...

Combination of Estradiol with Leukemia Inhibitory Factor Stimulates Granulosa Cells Differentiation into Oocyte-Like Cells.

Previous studies have documented that cumulus granulosa cells (GCs) can trans-differentiation into different non-ovarian cells, showing their multipo...

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation.

BACKGROUND AND OBJECTIVE: Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening pr...

Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers.

With the tremendous development of artificial intelligence, many machine learning algorithms have been applied to the diagnosis of human cancers. Rece...

Using an ontology of the human cardiovascular system to improve the classification of histological images.

The advantages of automatically recognition of fundamental tissues using computer vision techniques are well known, but one of its main limitations is...

Label-Free Quantification of Pharmacokinetics in Skin with Stimulated Raman Scattering Microscopy and Deep Learning.

The treatment of inflammatory skin conditions relies on a deep understanding of how drugs and tissue behave and interact. Although numerous methods ha...

Histomorphological investigation of intrahepatic connective tissue for surgical anatomy based on modern computer imaging analysis.

BACKGROUND/PURPOSE: Computer-assisted tissue imaging and analytical techniques were used to clarify the histomorphological structure of hepatic connec...

Needle tip force estimation by deep learning from raw spectral OCT data.

PURPOSE: Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback f...

Development and Validation of a Deep-learning Model to Assist With Renal Cell Carcinoma Histopathologic Interpretation.

OBJECTIVE: To develop and test the ability of a convolutional neural network (CNN) to accurately identify the presence of renal cell carcinoma (RCC) o...

The artificial intelligence-assisted cytology diagnostic system in large-scale cervical cancer screening: A population-based cohort study of 0.7 million women.

BACKGROUND: Adequate cytology is limited by insufficient cytologists in a large-scale cervical cancer screening. We aimed to develop an artificial int...