<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:DOC-HCMGYQBP</identifier><date>2024</date><creator>Ha, Chenyang</creator><relation>documents/doc/H/URN_NBN_SI_doc-HCMGYQBP_001.pdf</relation><relation>documents/doc/H/URN_NBN_SI_doc-HCMGYQBP_001.txt</relation><format format_type="issue">2</format><format format_type="volume">43</format><format format_type="type">article</format><format format_type="extent">str. 185-194</format><identifier identifier_type="DOI">10.5566/ias.3116</identifier><identifier identifier_type="ISSN">1580-3139</identifier><identifier identifier_type="COBISSID">245238275</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-HCMGYQBP</identifier><language>eng</language><publisher publisher_location="Ljubljana">Društvo za stereologijo in kvantitativno analizo slike, Medicinska fakulteta</publisher><source>Image analysis and stereology</source><rights>BY</rights><subject language_type_id="slv">globoko učenje</subject><subject language_type_id="slv">konvolucijske neuralne mreže</subject><subject language_type_id="slv">rak dojk</subject><subject language_type_id="slv">ultrazvok</subject><subject language_type_id="slv">Vision-Transformer</subject><title>A vision transformer network with wavelet-based features for breast ultrasound classification</title></Record>