20.500.12556/RUL-160506 Anotacija kolonoskopskih slik s temeljnimi modeli Colonoscopy image annotation with foundation models Anotacija slik je ključen, a večkrat zamuden korak pri pripravi slikovnih podatkovnih zbirk. Poleg grafičnega vmesnika anotacijskega orodja, na hitrost anotacije vplivajo implementirani segmentacijski pristopi. Z razvojem globokega učenja na področju računalniškega vida se je pojavila možnost nadomestitve ročne anotacije in tradicionalnih segmentacijskih algoritmov s hitrejšimi in bolj natančnimi pristopi. Eden takšnih je temeljni model Segment Anything, ki smo ga analizirali v večih različicah (ViT-b, ViT-l, ViT-h, MobileSAM, SAM-Med2D, MedSAM) in testirali na podatkovni zbirki kolonoskopskih slik Kvasir-SEG in kolonoskopskih inštrumentov Kvasir-Instrument. Ovrednotili smo natančnost segmentacije in časovno zahtevnost modelov z resničnimi maskami objektov in na podlagi rezultatov, implementirali funkcionalnosti najboljšega modela v prototipni anotacijski program. Image annotation is a crucial but often time-consuming step in preparing image datasets. In addition to the graphical interface of the annotation tool, the speed of annotation is influenced by the implemented segmentation approaches. With the development of deep learning in the field of computer vision, the possibility has arisen to replace manual annotation and traditional segmentation algorithms with faster and more accurate approaches. One such model is the foundation model Segment Anything, which we analyzed in various versions (ViT-b, ViT-l, ViT-h, MobileSAM, SAM-Med2D, MedSAM) and tested on the Kvasir-SEG dataset of colonoscopic images and the Kvasir-Instrument dataset of colonoscopic instruments. We evaluated the segmentation accuracy and time complexity of the models with ground-truth object masks and, based on the results, implemented the functionalities of the best model into a prototype annotation program. anotacija segmentacija globoko učenje Vision Transformer temeljni model Segment Anything kolonoskopija annotation segmentation deep learning Vision Transformer foundation model Segment Anything colonoscopy true false false Slovenski jezik Angleški jezik Diplomsko delo/naloga 2024-08-29 10:45:04 2024-08-29 10:45:07 2024-09-23 08:36:42 0000-00-00 00:00:00 2024 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1970-01-01 37324 208428803 Lazic_Miha_-_Anotacija_kolonoskopskih_slik_s_temeljnimi_modeli.pdf Lazic_Miha_-_Anotacija_kolonoskopskih_slik_s_temeljnimi_modeli.pdf 1 1633FB8A5A7A07F029F545AAEFE25FBF ae0cd8eddaa59cc47dd007d28e405a1000aad1ad22b3093d6a3422a5757d90a0 c320c2c6-65e2-11ef-8f74-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=189211 Fakulteta za računalništvo in informatiko 0 0 0