Pathology

A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images.

Mitosis counting of biopsies is an important biomarker for breast cancer patients, which supports disease prognostication and treatment planning. Developing a robust mitotic cell detection model is highly challenging due to its complex growth pattern and high similarities with non-mitotic cells. Most mitosis detection algorithms have poor generalizability across image domains and lack reproducibil...

The success rate of robotic natural orifice intracorporeal anastomosis and transrectal extraction (NICE procedure) in a large cohort of consecutive unselected patients.

BACKGROUND: The Robotic NICE procedure is a total intracorporeal natural orifice approach in which specimen extraction and anastomosis is accomplished without an abdominal wall incision other than the port sites themselves. We aim to present the success rate of the NICE procedure in a large cohort of unselected consecutive patients presenting with colorectal disease using a stepwise and reproducib...

Deep neural network-based structural health monitoring technique for real-time crack detection and localization using strain gauge sensors.

Structural health monitoring (SHM) techniques often require a large number of sensors to evaluate and monitor the structural health. In this paper, we...

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images.

Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological...

Bias reduction in representation of histopathology images using deep feature selection.

Appearing traces of bias in deep networks is a serious reliability issue which can play a significant role in ethics and generalization related concer...

White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS).

Treatment of blood smears with Wright's stain is one of the most helpful tools in detecting white blood cell abnormalities. However, to diagnose leuko...

Deep learning-based synapse counting and synaptic ultrastructure analysis of electron microscopy images.

BACKGROUND: Synapses are the connections between neurons in the central nervous system (CNS) or between neurons and other excitable cells in the perip...

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues.

Upcoming technologies enable routine collection of highly multiplexed (20-60 channel), subcellular resolution images of mammalian tissues for research...

Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm.

Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveol...

Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio.

Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements ...

Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging.

BACKGROUND: Upon the discovery of ovarian cysts, obstetricians, gynecologists, and ultrasound examiners must address the common clinical challenge of ...

Deep learning for computational cytology: A survey.

Computational cytology is a critical, rapid-developing, yet challenging topic in medical image computing concerned with analyzing digitized cytology i...

Convolutional neural network-based portable computer vision system for freshness assessment of crayfish (Prokaryophyllus clarkii).

Developing novel techniques for freshness assessment are of the utmost importance in yield and trade of aquatic products. The crayfish (Prokaryophyllu...

Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future.

Machine and deep learning techniques are prevalent in the medical discipline due to their high level of accuracy in disease diagnosis. One such diseas...

P-ResUnet: Segmentation of brain tissue with Purified Residual Unet.

Brain tissue of Magnetic Resonance Imaging is precisely segmented and quantified, which aids in the diagnosis of neurological diseases such as epileps...

Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records.

Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study a...

Diseased thyroid tissue classification in OCT images using deep learning: Towards surgical decision support.

Intraoperative guidance tools for thyroid surgery based on optical coherence tomography (OCT) could aid distinguish between normal and diseased tissue...

Bone tumor necrosis rate detection in few-shot X-rays based on deep learning.

Although biopsy-based necrosis rate is a golden standard for reflecting the sensitivity of bone tumor and guiding postoperative chemotherapy, it requi...

A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models.

Idiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively...

Assessment of Thermal Damage from Robot-Drilled Craniotomy for Cranial Window Surgery in Mice.

Cranial window surgery allows for the imaging of brain tissue in live mice with the use of multiphoton or other intravital imaging techniques. However...