Sökning: "Breast cancer classification"

Visar resultat 1 - 5 av 23 uppsatser innehållade orden Breast cancer classification.

  1. 1. Self-Supervised Learning for Tabular Data: Analysing VIME and introducing Mix Encoder

    Kandidat-uppsats, Lunds universitet/Fysiska institutionen

    Författare :Max Svensson; [2024]
    Nyckelord :Machine Learning; Self-supervised learning; AI; Physics; Medicine; Physics and Astronomy;

    Sammanfattning : 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. LÄS MER

  3. 2. Uncertainty Quantification in Deep Learning for Breast Cancer Classification in Point-of-Care Ultrasound Imaging

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Marisa Wodrich; [2024]
    Nyckelord :Uncertainty quantification; Deep learning; Breast cancer classification; Trustworthy AI; Point-of-care ultrasound; Mathematics and Statistics;

    Sammanfattning : 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. LÄS MER

  4. 3. Using Generative Adversarial Networks for H&E-to-HER2 Stain Translation in Digital Pathology Images

    Master-uppsats, Linköpings universitet/Institutionen för medicinsk teknik

    Författare :William Tirmén; [2023]
    Nyckelord :Machine learning; Artificial intelligence; Digital pathology; Image processing; Generative adversarial networks; Image-to-image translation;

    Sammanfattning : In digital pathology, hematoxylin & eosin (H&E) is a routine stain which is performed on most clinical cases and it often provides clinicians with sufficient information for diagnosis. However, when making decisions on how to guide breast cancer treatment, immunohistochemical staining of human epidermal growth factor 2 (HER2 staining) is also needed. LÄS MER

  5. 4. Evaluating Random Forest and k-Nearest Neighbour Algorithms on Real-Life Data Sets

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Atheer Salim; Milad Farahani; [2023]
    Nyckelord :Random Forest; k-Nearest Neighbour; Evaluation; Machine Learning; Classification; Execution Time; Slumpmässig Skog; k-Närmaste Granne; Utvärdering; Maskininlärning; Klassificiering; Exekveringstid;

    Sammanfattning : Computers can be used to classify various types of data, for example to filter email messages, detect computer viruses, detect diseases, etc. This thesis explores two classification algorithms, random forest and k-nearest neighbour, to understand how accurately and how quickly they classify data. LÄS MER

  7. 5. Kvinnors upplevelser av fysisk aktivitet under adjuvant cytostatikabehandling vid bröstcancer : en litteraturöversikt

    Kandidat-uppsats, Sophiahemmet Högskola

    Författare :Elina Cederberg Selander; Josefine Frykstrand; [2023]
    Nyckelord :Adjuvant chemotherapy; Breast cancer; Experiences; Physical activity; Self-care; Adjuvant cytostatikabehandling; Bröstcancer; Egenvård; Fysisk aktivitet; Upplevelser;

    Sammanfattning : Bakgrund Bröstcancer är den vanligaste cancerformen globalt bland kvinnor. Både den som insjuknat och dennes eventuella närstående drabbas av sjukdomens följder. En behandlingsform är adjuvant cytostatika vilket ofta resulterar i en mångfacetterad biverkningsprofil. LÄS MER