Pathology

Antibiotic therapy in patients with high prostate-specific antigen: Is it worth considering? A systematic review.

: To address the question of whether antibiotic therapy can obviate the need for prostate biopsy (PBx) in patients presenting with high prostate-specific antigen (PSA) levels. : With the increase in unnecessary PBx in men with high PSA levels, a systematic review was performed according to the Cochrane Reviews guidelines and in accordance with the Preferred Reporting Items for Systematic Reviews a...

Identification of genes of four malignant tumors and a novel prediction model development based on PPI data and support vector machines.

Triple-negative breast cancer (TNBC), colon adenocarcinoma (COAD), ovarian cancer (OV), and glioblastoma multiforme (GBM) are common malignant tumors, in which significant challenges are still faced in early diagnosis, treatment, and prognosis. Therefore, further identification of genes related to those malignant tumors is of great significance for the improvement of management of the diseases. Th...

Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis.

Despite the potential to revolutionise disease diagnosis by performing data-driven classification, clinical interpretability of ConvNet remains challe...

Texture analysis and multiple-instance learning for the classification of malignant lymphomas.

BACKGROUND AND OBJECTIVES: Malignant lymphomas are cancers of the immune system and are characterized by enlarged lymph nodes that typically spread ac...

Next generation pathology: artificial intelligence enhances histopathology practice.

Deep learning algorithms have shown benefits for pathology in the context of risk stratification of tumors. Although the results are promising, severa...

Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality.

Ovarian cancer (OC) is a common cause of cancer death among women worldwide, so there is a pressing need to identify factors influencing OC mortality....

CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine learning.

T lymphocytes are a group of cells representing the main effectors of human adaptive immunity. Characterization of the most representative T-lymphocyt...

Urine Sediment Recognition Method Based on Multi-View Deep Residual Learning in Microscopic Image.

Urine sediment recognition is attracting growing interest in the field of computer vision. A multi-view urine cell recognition method based on multi-v...

A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning.

Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of hum...

Developments of reproductive management and biotechnology in the pig.

This review aims to describe changes in production environment, management tools and technology to alleviate problems seen with the present hyperproli...

Breast cancer histopathology image classification through assembling multiple compact CNNs.

BACKGROUND: Breast cancer causes hundreds of thousands of deaths each year worldwide. The early stage diagnosis and treatment can significantly reduce...

Cascade and Fusion of Multitask Convolutional Neural Networks for Detection of Thyroid Nodules in Contrast-Enhanced CT.

With the development of computed tomography (CT), the contrast-enhanced CT scan is widely used in the diagnosis of thyroid nodules. However, due to th...

Radiomics in stratification of pancreatic cystic lesions: Machine learning in action.

Pancreatic cystic lesions (PCLs) are well-known precursors of pancreatic cancer. Their diagnosis can be challenging as their behavior varies from beni...

An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images.

BACKGROUND: Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the ima...

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.

PURPOSE: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease followin...

Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning.

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the mea...

Classification and Diagnosis of Thyroid Carcinoma Using Reinforcement Residual Network with Visual Attention Mechanisms in Ultrasound Images.

How to differentiate thyroid cancer nodules from a large number of benign nodules is always a challenging subject for clinicians. This paper proposes ...

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images.

We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual cor...

Ovarian Toxicity Assessment in Histopathological Images Using Deep Learning.

As ovarian toxicity is often a safety concern for cancer therapeutics, identification of ovarian pathology is important in early stages of preclinical...

Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment.

Background Men suspected of having clinically significant prostate cancer (sPC) increasingly undergo prostate MRI. The potential of deep learning to p...