Pathology

Deep learning in mammography and breast histology, an overview and future trends.

Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based CAD systems develo...

Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

PURPOSE: To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images.

A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer.

OBJECT: Pathologic prediction of prostate cancer can be made by predicting the patient's prostate metastasis prior to surgery based on biopsy informat...

Detecting and classifying lesions in mammograms with Deep Learning.

In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of ...

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clin...

Computational Sensing of Staphylococcus aureus on Contact Lenses Using 3D Imaging of Curved Surfaces and Machine Learning.

We present a cost-effective and portable platform based on contact lenses for noninvasively detecting Staphylococcus aureus, which is part of the huma...

Predicting cancer outcomes from histology and genomics using convolutional networks.

Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patien...

Advances in automated tongue diagnosis techniques.

Tongue diagnosis can be an effective, noninvasive method to perform an auxiliary diagnosis any time anywhere, which can support the global need in the...

Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls.

Colorectal cancer (CRC) is one of the most daunting diseases due to its increasing worldwide prevalence, which requires imperative development of mini...

Acral melanoma detection using a convolutional neural network for dermoscopy images.

BACKGROUND/PURPOSE: Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We ap...

Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering.

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends o...

Noninvasive detection of macrophage activation with single-cell resolution through machine learning.

We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters ...

Tissue classification and segmentation of pressure injuries using convolutional neural networks.

BACKGROUND AND OBJECTIVES: This paper presents a new approach for automatic tissue classification in pressure injuries. These wounds are localized ski...

Using machine-learning to optimize phase contrast in a low-cost cellphone microscope.

Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computat...

Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks.

BACKGROUND: Pathological angiogenesis has been identified in many malignancies as a potential prognostic factor and target for therapy. In most cases,...

Deep neural models for ICD-10 coding of death certificates and autopsy reports in free-text.

We address the assignment of ICD-10 codes for causes of death by analyzing free-text descriptions in death certificates, together with the associated ...

Agile convolutional neural network for pulmonary nodule classification using CT images.

OBJECTIVE: To distinguish benign from malignant pulmonary nodules using CT images is critical for their precise diagnosis and treatment. A new Agile c...

Automated Interpretation of Blood Culture Gram Stains by Use of a Deep Convolutional Neural Network.

Microscopic interpretation of stained smears is one of the most operator-dependent and time-intensive activities in the clinical microbiology laborato...

Machine learning-based diagnosis of melanoma using macro images.

Cancer bears a poisoning threat to human society. Melanoma, the skin cancer, originates from skin layers and penetrates deep into subcutaneous layers....

Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images.

Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size a...