HistopathologyHistopathology (compound of three Greek words: ἱστός histos 'tissue', πάθος pathos 'suffering', and -λογία -logia 'study of') refers to the microscopic examination of tissue in order to study the manifestations of disease. Specifically, in clinical medicine, histopathology refers to the examination of a biopsy or surgical specimen by a pathologist, after the specimen has been processed and histological sections have been placed onto glass slides. In contrast, cytopathology examines free cells or tissue micro-fragments (as "cell blocks").
Gastrointestinal cancerGastrointestinal cancer refers to malignant conditions of the gastrointestinal tract (GI tract) and accessory organs of digestion, including the esophagus, stomach, biliary system, pancreas, small intestine, large intestine, rectum and anus. The symptoms relate to the organ affected and can include obstruction (leading to difficulty swallowing or defecating), abnormal bleeding or other associated problems. The diagnosis often requires endoscopy, followed by biopsy of suspicious tissue.
Euler methodIn mathematics and computational science, the Euler method (also called the forward Euler method) is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value. It is the most basic explicit method for numerical integration of ordinary differential equations and is the simplest Runge–Kutta method. The Euler method is named after Leonhard Euler, who first proposed it in his book Institutionum calculi integralis (published 1768–1870).
PerceptronIn machine learning, the perceptron (or McCulloch-Pitts neuron) is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
Residual neural networkA Residual Neural Network (a.k.a. Residual Network, ResNet) is a deep learning model in which the weight layers learn residual functions with reference to the layer inputs. A Residual Network is a network with skip connections that perform identity mappings, merged with the layer outputs by addition. It behaves like a Highway Network whose gates are opened through strongly positive bias weights. This enables deep learning models with tens or hundreds of layers to train easily and approach better accuracy when going deeper.
Uterine cancerUterine cancer, also known as womb cancer, includes two types of cancer that develop from the tissues of the uterus. Endometrial cancer forms from the lining of the uterus, and uterine sarcoma forms from the muscles or support tissue of the uterus. Endometrial cancer accounts for approximately 90% of all uterine cancers in the United States. Symptoms of endometrial cancer include changes in vaginal bleeding or pain in the pelvis. Symptoms of uterine sarcoma include unusual vaginal bleeding or a mass in the vagina.
Naive Bayes classifierIn statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve high accuracy levels. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem.
Intestinal epitheliumThe intestinal epithelium is the single cell layer that form the luminal surface (lining) of both the small and large intestine (colon) of the gastrointestinal tract. Composed of simple columnar epithelial cells, it serves two main functions: absorbing useful substances into the body and restricting the entry of harmful substances. As part of its protective role, the intestinal epithelium forms an important component of the intestinal mucosal barrier. Certain diseases and conditions are caused by functional defects in the intestinal epithelium.
Fine-needle aspirationFine-needle aspiration (FNA) is a diagnostic procedure used to investigate lumps or masses. In this technique, a thin (23–25 gauge (0.52 to 0.64 mm outer diameter)), hollow needle is inserted into the mass for sampling of cells that, after being stained, are examined under a microscope (biopsy). The sampling and biopsy considered together are called fine-needle aspiration biopsy (FNAB) or fine-needle aspiration cytology (FNAC) (the latter to emphasize that any aspiration biopsy involves cytopathology, not histopathology).
