Pathology

Optical force estimation for interactions between tool and soft tissues.

Robotic assistance in minimally invasive surgery offers numerous advantages for both patient and surgeon. However, the lack of force feedback in robotic surgery is a major limitation, and accurately estimating tool-tissue interaction forces remains a challenge. Image-based force estimation offers a promising solution without the need to integrate sensors into surgical tools. In this indirect appro...

Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images.

Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (×30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean...

Deep Learning-Based Multiclass Brain Tissue Segmentation in Fetal MRIs.

Fetal brain tissue segmentation is essential for quantifying the presence of congenital disorders in the developing fetus. Manual segmentation of feta...

Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients.

The goal of this study was to employ novel deep-learning convolutional-neural-network (CNN) to predict pathological complete response (PCR), residual ...

A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI.

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed ...

4D segmentation of the thoracic aorta from 4D flow MRI using deep learning.

BACKGROUND: 4D flow MRI allows the analysis of hemodynamic changes in the aorta caused by pathologies such as thoracic aortic aneurysms (TAA). For per...

Robotic Simulators for Tissue Examination Training With Multimodal Sensory Feedback.

Tissue examination by hand remains an essential technique in clinical practice. The effective application depends on skills in sensorimotor coordinati...

Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics.

Dynamic contrast-enhanced MRI (DCE-MRI) is routinely included in the prostate MRI protocol for a long time; its role has been questioned. It provides ...

Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning.

Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and w...

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning.

Preliminary microbiological diagnosis usually relies on microscopic examination and, due to the routine culture and bacteriological examination, lasts...

Semi-Supervised Pixel Contrastive Learning Framework for Tissue Segmentation in Histopathological Image.

Accurate tissue segmentation in histopathological images is essential for promoting the development of precision pathology. However, the size of the d...

A Benchmark Dataset of Endoscopic Images and Novel Deep Learning Method to Detect Intestinal Metaplasia and Gastritis Atrophy.

Endoscopy has been routinely used to diagnose stomach diseases including intestinal metaplasia (IM) and gastritis atrophy (GA). Such routine examinati...

Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images.

IMPORTANCE: Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challengi...

DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning.

The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field. However, a p...

A reliable and low-cost deep learning model integrating convolutional neural network and transformer structure for fine-grained classification of chicken Eimeria species.

Chicken coccidiosis is a disease caused by Eimeria spp. and costs the broiler industry more than 14 billion dollars per year globally. Different chick...

Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models.

Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pa...

Integrated Damage Location Diagnosis of Frame Structure Based on Convolutional Neural Network with Inception Module.

Accurate damage location diagnosis of frame structures is of great significance to the judgment of damage degree and subsequent maintenance of frame s...

Feasibility and Accuracy of Robot-Assisted, Stereotactic Biopsy Using 3-Dimensional Intraoperative Imaging and Frameless Registration Tool.

BACKGROUND: Robot-assisted stereotactic biopsy is evolving: 3-dimensional intraoperative imaging tools and new frameless registration systems are spre...

Nystagmus patterns classification framework based on deep learning and optical flow.

Benign paroxysmal positional vertigo (BPPV) is the most common vestibular peripheral vertigo disease characterized by brief recurrent vertigo with pos...

Deep learning as a new tool in the diagnosis of mycosis fungoides.

Mycosis Fungoides (MF) makes up the most of the cutaneous lymphomas. As a malignant disease, the greatest diagnostical challenge is to timely differen...