Wykaz publikacji wybranego autora

Paweł Kłeczek, dr inż.

asystent

Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering
WEAIiIB-kair

  • 2018

    [dyscyplina 1] dziedzina nauk inżynieryjno-technicznych / automatyka, elektronika i elektrotechnika

Identyfikatory Autora Informacje o Autorze w systemach zewnętrznych

ORCID: 0000-0002-6652-7978 orcid iD

ResearcherID: D-2190-2016

Scopus: 56426336300

PBN: 5e7093da878c28a0473b1f8c

OPI Nauka Polska

Liczba pozycji spełniających powyższe kryteria selekcji: 18, z ogólnej liczby 19 publikacji Autora

1
  • A new approach to border irregularity assessment with application in skin pathology
2
3
  • Automated epidermis segmentation in histopathological images of human skin stained with hematoxylin and eosin
4
  • Automatic classification of specific melanocytic lesions using artificial intelligence
5
  • Automatic detection of blue-whitish veil as the primary dermoscopic feature
6
  • Classification of physiological data for emotion recognition
7
  • Deep neural networks and advanced computer vision algorithms in the early diagnosis of skin diseases
8
  • Detection and classification of pigment network in dermoscopic color images as one of the 7-point checklist criteria
9
  • Detection and classification of pigment network in dermoscopic color images as one of the 7-point checklist criteria
10
  • DokuWiki jako system do wygodnego tworzenia i udostępniania materiałów dydaktycznych
11
  • eSkin: study on the smartphone application for early detection of malignant melanoma
12
  • Melanoma thickness prediction based on convolutional neural network with VGG-19 model transfer learning
13
  • Pre-trained deep convolutional neural network for clostridioides difficile bacteria cytotoxicity classification based on fluorescence images
14
  • Region adjacency graph approach for acral melanocytic lesion segmentation
15
  • Segmentation of black ink and melanin in skin histopathological images
16
  • Semi-supervised nests of melanocytes segmentation method using convolutional autoencoders
17
  • The accuracy of H&E stain unmixing techniques when estimating relative stain concentration
18
  • The accuracy of H&E stain unmixing techniques when estimating relative stain concentrations