Matches in SemOpenAlex for { <https://semopenalex.org/work/W4310187910> ?p ?o ?g. }
- W4310187910 endingPage "4472" @default.
- W4310187910 startingPage "4472" @default.
- W4310187910 abstract "In recent years, deep learning has been successfully applied to medical image analysis and provided assistance to medical professionals. Machine learning is being used to offer diagnosis suggestions, identify regions of interest in images, or augment data to remove noise. Training models for such tasks require a large amount of labeled data. It is often difficult to procure such data due to the fact that these requires experts to manually label them, in addition to the privacy and legal concerns that limiting their collection. Due to this, creating self-supervision learning methods and domain-adaptation techniques dedicated to this domain is essential. This paper reviews concepts from the field of deep learning and how they have been applied to medical image analysis. We also review the current state of self-supervised learning methods and their applications to medical images. In doing so, we will also present the resource ecosystem of researchers in this field, such as datasets, evaluation methodologies, and benchmarks." @default.
- W4310187910 created "2022-11-30" @default.
- W4310187910 creator A5047951827 @default.
- W4310187910 creator A5057284368 @default.
- W4310187910 date "2022-11-27" @default.
- W4310187910 modified "2023-10-14" @default.
- W4310187910 title "Deep Learning Research Directions in Medical Imaging" @default.
- W4310187910 cites W1901129140 @default.
- W4310187910 cites W2064675550 @default.
- W4310187910 cites W2108598243 @default.
- W4310187910 cites W2126598020 @default.
- W4310187910 cites W2152177190 @default.
- W4310187910 cites W2163608936 @default.
- W4310187910 cites W2194775991 @default.
- W4310187910 cites W2254039850 @default.
- W4310187910 cites W2321533354 @default.
- W4310187910 cites W2344138609 @default.
- W4310187910 cites W2464708700 @default.
- W4310187910 cites W2526558307 @default.
- W4310187910 cites W2581082771 @default.
- W4310187910 cites W2592929672 @default.
- W4310187910 cites W2628684354 @default.
- W4310187910 cites W2734741635 @default.
- W4310187910 cites W2804643321 @default.
- W4310187910 cites W2909763647 @default.
- W4310187910 cites W2914493539 @default.
- W4310187910 cites W2922314919 @default.
- W4310187910 cites W2962767316 @default.
- W4310187910 cites W2962936819 @default.
- W4310187910 cites W2963470893 @default.
- W4310187910 cites W2964744899 @default.
- W4310187910 cites W2979708377 @default.
- W4310187910 cites W2979888373 @default.
- W4310187910 cites W3012086751 @default.
- W4310187910 cites W3025651738 @default.
- W4310187910 cites W3026598587 @default.
- W4310187910 cites W3036319923 @default.
- W4310187910 cites W3083622693 @default.
- W4310187910 cites W3091831727 @default.
- W4310187910 cites W3096609285 @default.
- W4310187910 cites W3096831136 @default.
- W4310187910 cites W3097065222 @default.
- W4310187910 cites W3102174132 @default.
- W4310187910 cites W3120430728 @default.
- W4310187910 cites W3159481202 @default.
- W4310187910 cites W3171007011 @default.
- W4310187910 cites W343636949 @default.
- W4310187910 cites W4212875960 @default.
- W4310187910 cites W4226351492 @default.
- W4310187910 cites W4312933868 @default.
- W4310187910 cites W66427752 @default.
- W4310187910 doi "https://doi.org/10.3390/math10234472" @default.
- W4310187910 hasPublicationYear "2022" @default.
- W4310187910 type Work @default.
- W4310187910 citedByCount "1" @default.
- W4310187910 countsByYear W43101879102023 @default.
- W4310187910 crossrefType "journal-article" @default.
- W4310187910 hasAuthorship W4310187910A5047951827 @default.
- W4310187910 hasAuthorship W4310187910A5057284368 @default.
- W4310187910 hasBestOaLocation W43101879101 @default.
- W4310187910 hasConcept C108583219 @default.
- W4310187910 hasConcept C115961682 @default.
- W4310187910 hasConcept C119857082 @default.
- W4310187910 hasConcept C120665830 @default.
- W4310187910 hasConcept C121332964 @default.
- W4310187910 hasConcept C127413603 @default.
- W4310187910 hasConcept C134306372 @default.
- W4310187910 hasConcept C139807058 @default.
- W4310187910 hasConcept C154945302 @default.
- W4310187910 hasConcept C188198153 @default.
- W4310187910 hasConcept C202444582 @default.
- W4310187910 hasConcept C206345919 @default.
- W4310187910 hasConcept C2522767166 @default.
- W4310187910 hasConcept C2776434776 @default.
- W4310187910 hasConcept C31258907 @default.
- W4310187910 hasConcept C31601959 @default.
- W4310187910 hasConcept C33923547 @default.
- W4310187910 hasConcept C36503486 @default.
- W4310187910 hasConcept C41008148 @default.
- W4310187910 hasConcept C78519656 @default.
- W4310187910 hasConcept C95623464 @default.
- W4310187910 hasConcept C9652623 @default.
- W4310187910 hasConcept C99498987 @default.
- W4310187910 hasConceptScore W4310187910C108583219 @default.
- W4310187910 hasConceptScore W4310187910C115961682 @default.
- W4310187910 hasConceptScore W4310187910C119857082 @default.
- W4310187910 hasConceptScore W4310187910C120665830 @default.
- W4310187910 hasConceptScore W4310187910C121332964 @default.
- W4310187910 hasConceptScore W4310187910C127413603 @default.
- W4310187910 hasConceptScore W4310187910C134306372 @default.
- W4310187910 hasConceptScore W4310187910C139807058 @default.
- W4310187910 hasConceptScore W4310187910C154945302 @default.
- W4310187910 hasConceptScore W4310187910C188198153 @default.
- W4310187910 hasConceptScore W4310187910C202444582 @default.
- W4310187910 hasConceptScore W4310187910C206345919 @default.
- W4310187910 hasConceptScore W4310187910C2522767166 @default.
- W4310187910 hasConceptScore W4310187910C2776434776 @default.
- W4310187910 hasConceptScore W4310187910C31258907 @default.