Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Brain tumorA brain tumor occurs when abnormal cells form within the brain. There are two main types of tumors: malignant tumors and benign (non-cancerous) tumors. These can be further classified as primary tumors, which start within the brain, and secondary tumors, which most commonly have spread from tumors located outside the brain, known as brain metastasis tumors. All types of brain tumors may produce symptoms that vary depending on the size of the tumor and the part of the brain that is involved.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Supervised learningSupervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).
Brain metastasisA brain metastasis is a cancer that has metastasized (spread) to the brain from another location in the body and is therefore considered a secondary brain tumor. The metastasis typically shares a cancer cell type with the original site of the cancer. Metastasis is the most common cause of brain cancer, as primary tumors that originate in the brain are less common. The most common sites of primary cancer which metastasize to the brain are lung, breast, colon, kidney, and skin cancer.
Pattern recognitionPattern recognition is the automated recognition of patterns and regularities in data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent pattern. PR has applications in statistical data analysis, signal processing, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.
Childhood cancerChildhood cancer is cancer in a child. About 80% of childhood cancer cases can be successfully treated thanks to modern medical treatments and optimal patient care. However, only about 10% of children diagnosed with cancer reside in high-income countries where the necessary treatments and care is available. Childhood cancer represents only about 1% of all types of cancers diagnosed in children and adults. For this reason, childhood cancer is often ignored in control planning, contributing to the burden of missed opportunities for its diagnoses and management in countries that are low- and mid-income.
Lung cancerLung cancer, also known as lung carcinoma, is a malignant tumor that begins in the lung. Lung cancer is caused by genetic damage to the DNA of cells in the airways, often caused by cigarette smoking or inhaling damaging chemicals. Damaged airway cells gain the ability to multiply unchecked, causing the growth of a tumor. Without treatment, tumors spread throughout the lung, damaging lung function. Eventually lung tumors metastasize, spreading to other parts of the body.
Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Feature (machine learning)In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.
Anaplastic lymphoma kinaseAnaplastic lymphoma kinase (ALK) also known as ALK tyrosine kinase receptor or CD246 (cluster of differentiation 246) is an enzyme that in humans is encoded by the ALK gene. Anaplastic lymphoma kinase (ALK) was originally discovered in 1994 in anaplastic large-cell lymphoma (ALCL) cells. ALCL is caused by a (2;5)(p23:q35) chromosomal translocation that generates the fusion protein NPM-ALK, in which the kinase domain of ALK is fused to the amino-terminal part of the nucleophosmin (NPM) protein.
Weak supervisionWeak supervision, also called semi-supervised learning, is a paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them. It is characterized by using a combination of a small amount of human-labeled data (exclusively used in more expensive and time-consuming supervised learning paradigm), followed by a large amount of unlabeled data (used exclusively in unsupervised learning paradigm).
MeningiomaMeningioma, also known as meningeal tumor, is typically a slow-growing tumor that forms from the meninges, the membranous layers surrounding the brain and spinal cord. Symptoms depend on the location and occur as a result of the tumor pressing on nearby tissue. Many cases never produce symptoms. Occasionally seizures, dementia, trouble talking, vision problems, one sided weakness, or loss of bladder control may occur. Risk factors include exposure to ionizing radiation such as during radiation therapy, a family history of the condition, and neurofibromatosis type 2.
Feature engineeringFeature engineering or feature extraction or feature discovery is the process of extracting features (characteristics, properties, attributes) from raw data. Due to deep learning networks, such as convolutional neural networks, that are able to learn it by itself, domain-specific- based feature engineering has become obsolete for vision and speech processing.
Cancer stagingCancer staging is the process of determining the extent to which a cancer has grown and spread. A number from I to IV is assigned, with I being an isolated cancer and IV being a cancer that has metastasized and spread from its origin. The stage generally takes into account the size of a tumor, whether it has invaded adjacent organs, how many regional (nearby) lymph nodes it has spread to (if any), and whether it has appeared in more distant locations (metastasized).
Unsupervised learningUnsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.
Benign tumorA benign tumor is a mass of cells (tumor) that does not invade neighboring tissue or metastasize (spread throughout the body). Compared to malignant (cancerous) tumors, benign tumors generally have a slower growth rate. Benign tumors have relatively well differentiated cells. They are often surrounded by an outer surface (fibrous sheath of connective tissue) or stay contained within the epithelium. Common examples of benign tumors include moles and uterine fibroids. Some forms of benign tumors may be harmful to health.
CancerCancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. These contrast with benign tumors, which do not spread. Possible signs and symptoms include a lump, abnormal bleeding, prolonged cough, unexplained weight loss, and a change in bowel movements. While these symptoms may indicate cancer, they can also have other causes. Over 100 types of cancers affect humans. Tobacco use is the cause of about 22% of cancer deaths.
Scale spaceScale-space theory is a framework for multi-scale signal representation developed by the computer vision, and signal processing communities with complementary motivations from physics and biological vision. It is a formal theory for handling image structures at different scales, by representing an image as a one-parameter family of smoothed images, the scale-space representation, parametrized by the size of the smoothing kernel used for suppressing fine-scale structures.
CrizotinibCrizotinib, sold under the brand name Xalkori among others, is an anti-cancer medication used for the treatment of non-small cell lung carcinoma (NSCLC). It acts as an ALK (anaplastic lymphoma kinase) and ROS1 (c-ros oncogene 1) inhibitor. Crizotinib is indicated for the treatment of metastatic non-small cell lung cancer (NSCLC) or relapsed or refractory, systemic anaplastic large cell lymphoma (ALCL) that is ALK-positive. It is also indicated for the treatment of unresectable, recurrent, or refractory inflammatory anaplastic lymphoma kinase (ALK)-positive myofibroblastic tumors (IMT).
