Pathology

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients.

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be stro...

A computer-aided diagnosis system for differentiation and delineation of malignant regions on whole-slide prostate histopathology image using spatial statistics and multidimensional DenseNet.

PURPOSE: Prostate cancer (PCa) is a major health concern in aging males, and proper management of the disease depends on accurately interpreting pathology specimens. However, reading prostatectomy histopathology slides, which is basically for staging, is usually time consuming and differs from reading small biopsy specimens, which is mainly used for diagnosis. Generally, each prostatectomy specime...

Deep Learning-Based Super-resolution Ultrasound Speckle Tracking Velocimetry.

Deep ultrasound localization microscopy (deep-ULM) allows sub-wavelength resolution imaging with deep learning. However, the injection of contrast age...

Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery. The existing workflow for intraoperative diagnosis ...

A personalized computational model predicts cancer risk level of oral potentially malignant disorders and its web application for promotion of non-invasive screening.

BACKGROUND: Despite their high accuracy to recognize oral potentially malignant disorders (OPMDs) with cancer risk, non-invasive oral assays are poor ...

A new deep learning method for image deblurring in optical microscopic systems.

Deconvolution is the most commonly used image processing method in optical imaging systems to remove the blur caused by the point-spread function (PSF...

Breast tumor classification through learning from noisy labeled ultrasound images.

PURPOSE: To train deep learning models to differentiate benign and malignant breast tumors in ultrasound images, we need to collect many training samp...

Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning.

Detecting crossovers in cryo-electron microscopy images of protein fibrils is an important step towards determining the morphological composition of a...

Membranous Nephropathy with Rapid Progression.

We report a 49-year-old man with microscopic hematuria, subnephrotic proteinuria, and rapidly progressive renal failure. His biopsy had features of Ph...

Differentiation of thyroid nodules on US using features learned and extracted from various convolutional neural networks.

Thyroid nodules are a common clinical problem. Ultrasonography (US) is the main tool used to sensitively diagnose thyroid cancer. Although US is non-i...

Diagnosis of Parathyroid Adenomas with New Ultrasound Imaging Modalities.

Ultrasound technology is becoming an integral part of diagnosing parathyroid adenomas. Careful ultrasound evaluation with b-mode, shear wave elastogra...

Machine Learning Methods for Computer-Aided Breast Cancer Diagnosis Using Histopathology: A Narrative Review.

Histopathology is a method used for breast cancer diagnosis. Machine learning (ML) methods have achieved success for supervised learning tasks in the ...

Image reconstruction for sub-sampled atomic force microscopy images using deep neural networks.

Undersampling is a simple but efficient way to increase the imaging rate of atomic force microscopy (AFM). One major challenge in this approach is tha...

A novel machine learning based approach for iPS progenitor cell identification.

Identification of induced pluripotent stem (iPS) progenitor cells, the iPS forming cells in early stage of reprogramming, could provide valuable infor...

Presence of pyrroloquinazoline alkaloid in attenuates inflammatory response through the downregulation of pro-inflammatory mediators in LPS stimulated RAW 264.7 macrophages.

Inflammation is the primary response caused due to harmful stimuli which are followed by the increased draining of plasma and immune cells from the b...

Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification.

Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when de...

Learning the Macroscopic Flow Model of Short Fiber Suspensions from Fine-Scale Simulated Data.

Fiber-fiber interaction plays an important role in the evolution of fiber orientation in semi-concentrated suspensions. Flow induced orientation in sh...

Visual Servoed Robotic Mouse Oocyte Rotation.

UNLABELLED: Both injection and biopsy of a mammalian cell require positioning and orientation of a biological cell in a three-dimensional space under ...

Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.

Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create diff...

Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks.

Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detect...