AIMC Topic: Cell Nucleus
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High-Resolution Raman Microscopic Detection of Follicular Thyroid Cancer Cells with Unsupervised Machine Learning.
The journal of physical chemistry. B
May 8, 2019
We use Raman microscopic images with high spatial and spectral resolution to investigate differences between human follicular thyroid (Nthy-ori 3-1) and follicular thyroid carcinoma (FTC-133) cells, a well-differentiated thyroid cancer. Through compa...
Binary tree-like network with two-path Fusion Attention Feature for cervical cell nucleus segmentation.
Computers in biology and medicine
Mar 29, 2019
Early detection of cervical lesion is of great significance in reducing mortality from cervical cancer, and segmentation of cervical cell nuclei plays an important role in screening for cervical lesion. Compared with traditional algorithms, several d...
Bone Marrow Cells Detection: A Technique for the Microscopic Image Analysis.
Journal of medical systems
Feb 23, 2019
In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which consists of three modules including localization,...
Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images.
IEEE transactions on bio-medical engineering
Feb 22, 2019
OBJECTIVE: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 immunohistochemistry stained images. Although many automated methods have been proposed, most use a multi-stage processing pipeli...
Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data.
International journal of computer assisted radiology and surgery
Feb 16, 2019
PURPOSE: Cancers are almost always diagnosed by morphologic features in tissue sections. In this context, machine learning tools provide new opportunities to describe tumor immune cell interactions within the tumor microenvironment and thus provide p...
Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection.
IEEE transactions on medical imaging
Jan 25, 2019
Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e., varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduri...
Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening.
PLoS computational biology
Nov 30, 2018
Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction...
Sparse Representation Over Learned Dictionaries on the Riemannian Manifold for Automated Grading of Nuclear Pleomorphism in Breast Cancer.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Oct 22, 2018
Breast cancer is found to be the most pervasive type of cancer among women. Computer aided detection and diagnosis of cancer at the initial stages can increase the chances of recovery and thus reduce the mortality rate through timely prognosis and ad...
Automated detection of cancer cells in effusion specimens by DNA karyometry.
Cancer cytopathology
Oct 19, 2018
BACKGROUND: The average sensitivity of conventional cytology for the identification of cancer cells in effusion specimens is only approximately 58%. DNA image cytometry (DNA-ICM), which exploits the DNA content of morphologically suspicious nuclei me...
Deep learning nuclei detection: A simple approach can deliver state-of-the-art results.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Sep 17, 2018
BACKGROUND: Deep convolutional neural networks have become a widespread tool for the detection of nuclei in histopathology images. Many implementations share a basic approach that includes generation of an intermediate map indicating the presence of ...
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