Pathology

Unsupervised machine learning using an imaging mass spectrometry dataset automatically reassembles grey and white matter.

Current histological and anatomical analysis techniques, including fluorescence in situ hybridisation, immunohistochemistry, immunofluorescence, immunoelectron microscopy and fluorescent fusion protein, have revealed great distribution diversity of mRNA and proteins in the brain. However, the distributional pattern of small biomolecules, such as lipids, remains unclear. To this end, we have develo...

CIGuide: in situ augmented reality laser guidance.

PURPOSE : A robotic intraoperative laser guidance system with hybrid optic-magnetic tracking for skull base surgery is presented. It provides in situ augmented reality guidance for microscopic interventions at the lateral skull base with minimal mental and workload overhead on surgeons working without a monitor and dedicated pointing tools. METHODS : Three components were developed: a registration...

Machine Learning for Rapid Magnetic Resonance Fingerprinting Tissue Property Quantification.

Magnetic Resonance Fingerprinting (MRF) is an MRI-based method that can provide quantitative maps of multiple tissue properties simultaneously from a ...

Post-processing radio-frequency signal based on deep learning method for ultrasonic microbubble imaging.

BACKGROUND: Improving imaging quality is a fundamental problem in ultrasound contrast agent imaging (UCAI) research. Plane wave imaging (PWI) has been...

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data.

Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images...

nanite: using machine learning to assess the quality of atomic force microscopy-enabled nano-indentation data.

BACKGROUND: Atomic force microscopy (AFM) allows the mechanical characterization of single cells and live tissue by quantifying force-distance (FD) da...

LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs.

Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA-disease associ...

Enhancement of Acoustic Microscopy Lateral Resolution: A Comparison Between Deep Learning and Two Deconvolution Methods.

Scanning acoustic microscopy (SAM) provides high-resolution images of biological tissues. Since higher transducer frequencies limit penetration depth,...

Machine learning applications in the diagnosis of leukemia: Current trends and future directions.

Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Di...

Utilizing supervised machine learning to identify microglia and astrocytes in situ: implications for large-scale image analysis and quantification.

BACKGROUND: The evaluation of histological tissue samples plays a crucial role in deciphering preclinical disease and injury mechanisms. High-resoluti...

Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks.

Accurate diagnosis of thyroid nodules using ultrasonography is a valuable but tough task even for experienced radiologists, considering both benign an...

Deep Learning-Based Histopathologic Assessment of Kidney Tissue.

BACKGROUND: The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolution...

Gabor wavelet-based deep learning for skin lesion classification.

Skin cancer cases are increasing and becoming one of the main problems worldwide. Skin cancer is known as a malignant type of skin lesion, and early d...

Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features.

To predict lung nodule malignancy with a high sensitivity and specificity for low dose CT (LDCT) lung cancer screening, we propose a fusion algorithm ...

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.

Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns...

Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis.

Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent t...

A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets.

OBJECTIVE: High-throughput gene expression profiling of tissue samples from patients with systemic sclerosis (SSc) has identified 4 "intrinsic" gene e...

The performance of gravity-driven membrane (GDM) filtration for roofing rainwater reuse: Implications of roofing rainwater energy and rainwater purification.

Rainwater harvesting (RWH) coupled with gravity-driven membrane (GDM) filtration was used to simultaneously treat rainwater and recover energy. A pilo...

RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification.

The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various siz...

Robotic platform for microinjection into single cells in brain tissue.

Microinjection into single cells in brain tissue is a powerful technique to study and manipulate neural stem cells. However, such microinjection requi...