Medical research

Deep learning accurately stains digital biopsy slides

Tissue biopsy slides stained using hematoxylin and eosin (H&E) dyes are a cornerstone of histopathology, especially for pathologists needing to diagnose and determine the stage of cancers. A research team led by MIT scientists ...


AI unlocks rhythms of 'deep sleep'

Algorithms and deep learning has enabled Flinders University sleep researchers to dive deep into one of the mysteries of sleep health.

Diseases, Conditions, Syndromes

New technology supports COVID-19 testing within seconds

The knowhow from the world's top experts in lung ultrasound were collected and organized in the software application. Their expertise is now quickly available to the medical community, in a functional way, free of charge, ...

Radiology & Imaging

International research improves quality of CT scan imagery

Computerized tomography (CT) is one of the best medical tests for analyzing the effects of many illnesses, including COVID-19. An international team has developed a new method that improves the quality of the images obtained ...

Diseases, Conditions, Syndromes

AI auto-scans lung X-rays for coronavirus

Coronavirus can be identified automatically from sick patients' lung X-rays using artificial intelligence (AI) developed at Brunel University London.

Psychology & Psychiatry

A simple screening test for depression shows its validity

An analysis published in Psychotherapy and Psychosomatics indicates the value of the Patient Health Questionnaire-9 for screening depression. Screening for major depression with the Patient Health Questionnaire-9 (PHQ-9) ...

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In mathematics, computing, linguistics, and related subjects, an algorithm is a finite sequence of instructions, an explicit, step-by-step procedure for solving a problem, often used for calculation and data processing. It is formally a type of effective method in which a list of well-defined instructions for completing a task, will when given an initial state, proceed through a well-defined series of successive states, eventually terminating in an end-state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as probabilistic algorithms, incorporate randomness.

A partial formalization of the concept began with attempts to solve the Entscheidungsproblem (the "decision problem") posed by David Hilbert in 1928. Subsequent formalizations were framed as attempts to define "effective calculability" (Kleene 1943:274) or "effective method" (Rosser 1939:225); those formalizations included the Gödel-Herbrand-Kleene recursive functions of 1930, 1934 and 1935, Alonzo Church's lambda calculus of 1936, Emil Post's "Formulation 1" of 1936, and Alan Turing's Turing machines of 1936–7 and 1939.

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