Pathology

Diffusion histology imaging differentiates distinct pediatric brain tumor histology.

High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in their clinical management. We emp...

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses.

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features a...

Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders.

Advances in digital pathology have allowed a number of opportunities such as decision support using artificial intelligence (AI). The application of A...

Double-flow convolutional neural network for rapid large field of view Fourier ptychographic reconstruction.

Fourier ptychographic microscopy is a promising imaging technique which can circumvent the space-bandwidth product of the system and achieve a reconst...

Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology-A Machine Learning Approach to Histopathology.

Several deep learning approaches have been proposed to address the challenges in computational pathology by learning structural details in an unbiased...

Deep Consensus Network: Aggregating predictions to improve object detection in microscopy images.

Detection of cells and particles in microscopy images is a common and challenging task. In recent years, detection approaches in computer vision achie...

Machine Learning-based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study.

Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine lear...

A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images.

INTRODUCTION: We designed 5 convolutional neural network (CNN) models and ensemble models to differentiate malignant and benign thyroid nodules on CT,...

Applications of Computational Pathology in Head and Neck Cytopathology.

BACKGROUND: The application of computational technology to head and neck cytology material has been explored experimentally in several areas with a va...

Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling.

The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular p...

Robotic natural-orifice IntraCorporeal anastomosis with Extraction (NICE procedure) for complicated diverticulitis.

BACKGROUND: Totally intracorporeal surgery for left-sided resection carries numerous potential advantages by avoiding crossing staple lines and elimin...

Colorectal polyp characterization with standard endoscopy: Will Artificial Intelligence succeed where human eyes failed?

The American Society for Gastrointestinal Endoscopy (ASGE) has proposed the "resect-and-discard" and "diagnose-and-leave" strategies for diminutive co...

Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study.

BACKGROUND: The efficacy of artificial intelligence (AI)-driven automated medical-history-taking systems with AI-driven differential-diagnosis lists o...

Deep learning based digital cell profiles for risk stratification of urine cytology images.

Urine cytology is a test for the detection of high-grade bladder cancer. In clinical practice, the pathologist would manually scan the sample under th...

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images.

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often ...

Automated classification of cancer morphology from Italian pathology reports using Natural Language Processing techniques: A rule-based approach.

Pathology reports represent a primary source of information for cancer registries. Hospitals routinely process high volumes of free-text reports, a va...

Adaptive optics for structured illumination microscopy based on deep learning.

Structured illumination microscopy (SIM) is widely used in biological imaging for its high resolution, fast imaging speed, and simple optical setup. H...

An adaptive digital stain separation method for deep learning-based automatic cell profile counts.

BACKGROUND: Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in the fields...

3D microscopy and deep learning reveal the heterogeneity of crown-like structure microenvironments in intact adipose tissue.

Crown-like structures (CLSs) are adipose microenvironments of macrophages engulfing adipocytes. Their histological density in visceral adipose tissue ...

Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.

OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its p...