This parameter controls the occurence and strength of tissue ripping.

The NuCLS dataset contains over 220.000 annotations of cell nuclei in breast cancers. We show how to use these data to create a multi-rater model with the MIScnn Framework to automate the analysis of cell nuclei. For the model creation, we use the widespread U-Net approach embedded in a pipeline. This pipeline provides besides the high performance convolution neural network, several preprocessor techniques and a extended data exploration. The final model is tested in the evaluation phase using a wide variety of metrics with a subsequent visualization. Finally, the results are compared and interpreted with the results of the NuCLS study. As an outlook, indications are given which are important for the future development of models in the context of cell nuclei.

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features.

In situ sequencing enables gene activity inside body tissues to be depicted in microscope images. To facilitate interpretation of the vast quantities of information generated. Researchers have now developed an entirely new method of image analysis. Based on algorithms used in artificial intelligence, the method was originally devised to enhance understanding of social networks.

Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistently demonstrated to correlate with prognosis. We present a morphologic analysis of histology sections from 209 DLBCL cases with associated clinical and cytogenetic data. Duplicate tissue core sections were arranged in tissue microarrays (TMAs), and replicate sections were stained with H&E and immunohistochemical stains for CD10, BCL6, MUM1, BCL2, and MYC. The TMAs are accompanied by pathologist-annotated regions-of-interest (ROIs) that identify areas of tissue representative of DLBCL. We used a deep learning model to segment all tumor nuclei in the ROIs, and computed several geometric features for each segmented nucleus. We fit a Cox proportional hazards model to demonstrate the utility of these geometric features in predicting survival outcome, and found that it achieved a C-index (95% CI) of 0.635 (0.574,0.691). Our finding suggests that geometric features computed from tumor nuclei are of prognostic importance, and should be validated in prospective studies.

Given the crucial role of microtubules for cell survival, many researchers have found success using microtubule-targeting agents in the search for effective cancer therapeutics. Understanding microtubule responses to targeted interventions requires that the microtubule network within cells can be consistently observed across a large sample of images. However, fluorescence noise sources captured simultaneously with biological signals while using wide-field microscopes can obfuscate fine microtubule structures. Such requirements are particularly challenging for high-throughput imaging, where researchers must make decisions related to the tradeoff between imaging quality and speed. Here, we propose a computational framework to enhance the quality of high-throughput imaging data to achieve fast speed and high quality simultaneously. Using CycleGAN, we learn an image model from low-throughput, high-resolution images to enhance features, such as microtubule networks in high-throughput low-resolution images. We show that CycleGAN is effective in identifying microtubules with 0.93 AUC-ROC and that these results are robust to different kinds of image noise. We further apply CycleGAN to quantify the changes in microtubule density as a result of the application of drug compounds, and show that the quantified responses correspond well with known drug effects.

This is a Lasso; it is used to pick and capture animals. As a non-native English speaker, my first exposure to this word is in supervised learning. In this LASSO data science tutorial, we discuss the strengths of the Lasso logistic regression by stepping through how to apply this useful statistical method for classification problems in R and how the Lasso can be "similarly" used to pick and select input variables that are relevant to the classification problem at hand. Data analysts and data scientists use different regression methods for different kinds of analytics problems. One of the most talked-about methods is the Lasso.

University of Toronto Engineering researchers have built a set of magnetic'tweezers' that can position a nano-scale bead inside a human cell in three dimensions with unprecedented precision. The nano-bot has already been used to study the properties of cancer cells, and could point the way toward enhanced diagnosis and treatment. Professor Yu Sun and his team have been building robots that can manipulate individual cells for two decades. Their creations have the ability to manipulate and measure single cells--useful in procedures such as in vitro fertilization and personalized medicine. Their latest study, published today in Science Robotics, takes the technology one step further.

Fluorescence microscopy usually involves a trade-off between producing a quality image and having a healthy sample. Illuminating the sample with higher laser power strengthens the fluorescent signal but risks damaging biological samples and photobleaching fluorescent dyes. Imaging at a slower frame rate with lower laser power often produces high-quality images but sacrifices information in samples that move. When such compromises hinder the recording of high-quality images, researchers often try to improve the images after the fact. To that end, Loïc Royer at the Chan Zuckerberg Biohub in San Francisco and Martin Weigert, Florian Jug, and Eugene Myers at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany, have developed content-aware image restoration (CARE), a convolutional neural network trained on features specific to the system being observed.

Three, it turns out, is better than one. At least that's how it worked for a trio of former rivals who teamed up to claim the just-announced top prize in this year's Data Science Bowl. The fourth annual event focused on one of healthcare's most pressing problems -- the soaring cost and time needed to discover new drugs. A record-setting 18,000 participants battled over 90 days to deliver a deep learning algorithm to accelerate a crucial step in the drug-discovery pipeline: identifying the nucleus of each cell. This year's Data Science Bowl was "driven by a very real need to develop new treatments faster and more accurately," said Anne Carpenter, director of the imaging platform at the Broad Institute of MIT and Harvard, the nonprofit partner for the contest.