Pathology

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends.

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate st...

Deep Learning Enhanced Electrochemiluminescence Microscopy.

Limited by the efficiency of electrochemiluminescence, tens of seconds of exposure time are typically required to get a high-quality image. Image enhancement of short exposure time images to obtain a well-defined electrochemiluminescence image can meet the needs of high-throughput or dynamic imaging. Here, we propose deep enhanced ECL microscopy (DEECL), a general strategy that utilizes artificial...

Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images.

BACKGROUND: Applying deep learning to digital histopathology is hindered by the scarcity of manually annotated datasets. While data augmentation can a...

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning.

The quantitative analysis of subcellular organelles such as mitochondria in cell fluorescence microscopy images is a demanding task because of the inh...

A Two-Stage End-to-End Deep Learning Framework for Pathologic Examination in Skin Tumor Diagnosis.

Neurofibromas (NFs), Bowen disease (BD), and seborrheic keratosis (SK) are common skin tumors. Pathologic examination is the gold standard for diagnos...

Deep learning analysis of endometrial histology as a promising tool to predict the chance of pregnancy after frozen embryo transfers.

PURPOSE: Endometrial histology on hematoxylin and eosin (H&E)-stained preparations provides information associated with receptivity. However, traditio...

AtomVision: A Machine Vision Library for Atomistic Images.

Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose Atom...

Application of Deep Learning System Technology in Identification of Women's Breast Cancer.

: The classification of breast cancer is performed based on its histological subtypes using the degree of differentiation. However, there have been lo...

Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods.

Membranous nephropathy is the main cause of nephrotic syndrome, which has an insidious onset and may progress to end-stage renal disease with a high m...

Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder.

A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved th...

A real-time interpretable artificial intelligence model for the cholangioscopic diagnosis of malignant biliary stricture (with videos).

BACKGROUND AND AIMS: It is crucial to accurately determine malignant biliary strictures (MBSs) for early curative treatment. This study aimed to devel...

Deep learning in digital pathology for personalized treatment plans of cancer patients.

Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only ben...

Diagnosing malignant distal bile duct obstruction using artificial intelligence based on clinical biomarkers.

Diagnosing distal bile duct obstruction remains challenging. This study aimed to examine the diagnostic ability of artificial intelligence (AI) based ...

Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques.

Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor and the third cause of cancer-related deaths worldwide. For many years, the ...

The devil is in the details: a small-lesion sensitive weakly supervised learning framework for prostate cancer detection and grading.

Prostate cancer (PCa) is a significant health concern in aging males, and the diagnosis depends primarily on histopathological assessments to determin...

Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging.

Single-molecule localization microscopy in a typical wide-field setup has been widely used for investigating subcellular structures with super resolut...

Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.

The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal ...

Clinical applications of deep learning in breast MRI.

Deep learning (DL) is one of the most powerful data-driven machine-learning techniques in artificial intelligence (AI). It can automatically learn fro...

Non-destructive classification of unlabeled cells: Combining an automated benchtop magnetic resonance scanner and artificial intelligence.

In order to treat degenerative diseases, the importance of advanced therapy medicinal products has increased in recent years. The newly developed trea...

Robotic Single-Site Hysterectomy in Gynecologic Benign Pathology: A Systematic Review of the Literature.

Total hysterectomy is one of the most common gynecologic surgical procedures and it is mainly performed for benign pathologies. The introduction of r...