AIMC Topic: Microscopy

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Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.

The Lancet. Oncology
BACKGROUND: Detecting microsatellite instability (MSI) in colorectal cancer is crucial for clinical decision making, as it identifies patients with differential treatment response and prognosis. Universal MSI testing is recommended, but many patients...

BeadNet: deep learning-based bead detection and counting in low-resolution microscopy images.

Bioinformatics (Oxford, England)
MOTIVATION: An automated counting of beads is required for many high-throughput experiments such as studying mimicked bacterial invasion processes. However, state-of-the-art algorithms under- or overestimate the number of beads in low-resolution imag...

Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning.

Optics express
A transmission hyperspectral microscopic imager (THMI) that utilizes machine learning algorithms for hyperspectral detection of microalgae is presented. The THMI system has excellent performance with spatial and spectral resolutions of 4 µm and 3 nm,...

Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue.

Molecular imaging and biology
PURPOSE: Histological analysis of artery tissue samples is a widely used method for diagnosis and quantification of cardiovascular diseases. However, the variable and labor-intensive tissue staining procedures hinder efficient and informative histolo...

Classification of cell morphology with quantitative phase microscopy and machine learning.

Optics express
We describe and compare two machine learning approaches for cell classification based on label-free quantitative phase imaging with transport of intensity equation methods. In one approach, we design a multilevel integrated machine learning classifie...

An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study.

The Lancet. Digital health
BACKGROUND: There is high demand to develop computer-assisted diagnostic tools to evaluate prostate core needle biopsies (CNBs), but little clinical validation and a lack of clinical deployment of such tools. We report here on a blinded clinical vali...

Multi-Target Deep Learning for Algal Detection and Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Water quality has a direct impact on industry, agriculture, and public health. Algae species are common indicators of water quality. It is because algal communities are sensitive to changes in their habitats, giving valuable knowledge on variations i...

Prediction for Morphology and States of Stem Cell Colonies using a LSTM Network with Progressive Training Microscopy Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We present a new LSTM (P-LSTM: Progressive LSTM) network, aiming to predict morphology and states of cell colonies from time-lapse microscopy images. Apparent short-term changes occur in some types of time-lapse cell images. Therefore, long-term-memo...

Recognizing Magnification Levels in Microscopic Snapshots.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Recent advances in digital imaging has transformed computer vision and machine learning to new tools for analyzing pathology images. This trend could automate some of the tasks in the diagnostic pathology and elevate the pathologist workload. The fin...