Essays about: "breast cancer with deep learning"
Showing result 1 - 5 of 16 essays containing the words breast cancer with deep learning.
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1. Self-Supervised Learning for Tabular Data: Analysing VIME and introducing Mix Encoder
University essay from Lunds universitet/Fysiska institutionenAbstract : We introduce Mix Encoder, a novel self-supervised learning framework for deep tabular data models based on Mixup [1]. Mix Encoder uses linear interpolations of samples with associated pretext tasks to form useful pre-trained representations. READ MORE
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2. Uncertainty Quantification in Deep Learning for Breast Cancer Classification in Point-of-Care Ultrasound Imaging
University essay from Lunds universitet/Matematik LTHAbstract : Breast cancer is the most common type of cancer worldwide with an estimate of 2.3 million new cases in 2020, and the number one cause of cancer-related deaths in women. READ MORE
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3. Automatic Detection of Tumour Infiltrating Lymphocytes in Breast Cancer Whole Slide Images
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Cancer is one of the most common diseases this century, with breast cancer being the most common form. Pathological examination is used to detect and quantify Tumour-infiltrating lymphocytes (TILs) in breast cancer Whole Slide Images (WSIs), which can be done manually or automatically. READ MORE
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4. Lesion Segmentation in 3D FDG-PET/CT Scans Using Deep Learning
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Drug development is an expensive, long and complex process for pharmaceutical companies all around the world. One way to make it more reliable is to evaluate the drug at its early stages, so the decision can be made to go forward or move to another drug, which is also a time-consuming and costly task to be done by human experts. READ MORE
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5. Breast Cancer Histological Grading Using Graph Convolutional Networks
University essay from KTH/Matematik (Avd.)Abstract : Technological advancements have opened up the possibility of digitizing the pathological landscape, enabling deep learning-based methods to analyze digitized tissue samples, i.e., whole slide images (WSIs). Attention has recently shifted toward modeling WSIs as graphs since graph representations can capture dynamic relationships. READ MORE