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Showing 1 to 8 of 8 for “"Biomedical images"”.

  1. Certifying homological algorithms to study biomedical images

    En esta tesis se aborda el problema de la verificación de programas para el procesamiento homológico de imágenes biomédicas. Concretamente, se formalizan en la herramienta de demostración Coq/SSReflect algoritmos para el cálculo de grupos de homología, lo que produce programas ejecutables que son …

    dialnet Repository record for Certifying homological algorithms to study biomedical images (opens in a new tab)

  2. Restoration methods for biomedical images in confocal microscopy

    Diese Arbeit stellt neue Loesungen zum Problem Bildrestauration im biomedizinischen Bereich vor. Das Konfokal-Mikroskop ist eine verhaeltnismaessig neue Bildungstechnik, die als Standardwerkzeug in biomedizinischen Studien eingesetzt wird. Diese Technik dient zum Sammeln einer Reihe von 2D Bildern …

    tu-berlin Repository record for Restoration methods for biomedical images in confocal microscopy (opens in a new tab)

  3. Processing biomedical images for the study of treatments related to neurodegenerative diseases

    … to determine where neurons are located in large images and to ascertain which are the best features to describe this kind of cells.

    dialnet Repository record for Processing biomedical images for the study of treatments related to neurodegenerative diseases (opens in a new tab)

  4. NoiseLearner: An Unsupervised, Content-agnostic Approach to Detect Deepfake Images

    … in the improvement of hyper- realistic synthetic images or "deepfakes" at high resolutions, making them almost indistin- guishable from real images from cameras. While exciting, this technology introduces room for abuse. Deepfakes have already been misused to produce pornography, political …

    vt Repository record for NoiseLearner: An Unsupervised, Content-agnostic Approach to Detect Deepfake Images (opens in a new tab)

  5. Error Resilient Video Coding Using Bitstream Syntax And Iterative Microscopy Image Segmentation

    … challenge in image processing is the analysis of biomedical images acquired using optical microscopy. Due to the size and complexity of the images, automated segmentation methods are required to obtain quantitative, objective and reproducible measurements of biological entities. In this thesis, we …

    purdue-thes Repository record for Error Resilient Video Coding Using Bitstream Syntax And Iterative Microscopy Image Segmentation (opens in a new tab)

  6. Using Artificial Life to Design Machine Learning Algorithms for Decoding Gene Expression Patterns from Images

    … to decoding gene expression patterns from images. If analysis of these images proves successful, then this can be applied to real biomedical images reducing the limitations of invasive imaging. The results showed that the box counting dimension was a suitable feature extraction method …

    vt Repository record for Using Artificial Life to Design Machine Learning Algorithms for Decoding Gene Expression Patterns from Images (opens in a new tab)

  7. Graph Representation Learning to Study the Tumour Microenvironment

    … allow the acquisition of highly multiplexed biomedical images (HMBI) that generate spatial tissue maps of dozens of proteins capturing the intricacies of the TME. Combining the multidimensional cell phenotypes acquired with their spatial organization to predict clinically relevant information …

    cambridge Repository record for Graph Representation Learning to Study the Tumour Microenvironment (opens in a new tab)

  8. The effect of scaffold structure and biologic factors on bone regeneration in cap-based scaffolds measured using machine learning

    … segmented micro-computed tomography (Micro-CT) images of implanted scaffolds, a challenging segmentation problem because of the similar mineral composition, and thus attenuation, between scaffold and ingrown bone. Convolutional U-Net models are increasingly used for segmentation of biomedical

    uiuc Repository record for The effect of scaffold structure and biologic factors on bone regeneration in cap-based scaffolds measured using machine learning (opens in a new tab)