3 research outputs found

    Classification of Histological Images of the Intestine

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    Tema ovog rada je klasifikacija Crohnove bolesti ili ulceroznog kolitisa na temelju histolo┼íkih snimki crijeva pomo─çu dubokih neuronskih mre┼ża. Rad obja┼ínjava postupak predobrade malog, visokodimenzionalnog i neuravnote┼żenog skupa podataka kao ┼íto su segmentacija i razlamanja snimki na zakrpe radi smanjenja dimenzija ulaza, o─Źuvanja detalja, eliminacije ┼íuma te pove─çanja primjeraka u skupu podataka te uravnote┼żavanje skupa podataka i podjelu skupa podataka na skup za treniranje i testiranje. Opisuju se arhitektura modela, proces treniranja i evaluacije modela na temelju odabranih metrika. Metrike pokrivaju razne zahtjeve koje bi lije─Źnik mogao imati pri evaluaciji modela te dopu┼íta stru─Źnjaku da odabere najuspje┼íniji model.The topic of this paper is the classification of Crohn's disease or ulcerative colitis based on histological images of the intestine using deep neural networks. The paper explains the preprocessing procedure of a small, high-dimensional and imbalanced dataset, such as segmentation and splitting of recordings into patches in order to reduce input dimensions, preserve details, eliminate noise and increase the number of samples in the data set, as well as balancing the data set and dividing the data set into training and testing sets. The architecture of the model, the process of training and evaluation of the model based on selected metrics are described. The metrics cover the various requirements a physician might have when evaluating a model and allow the practitioner to select the most successful model

    Improving of Detection Results Through Variation of Object Appearance

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    Tema ovog rada je pobolj┼íanje rezultata procjene dubinske udaljenosti objekta od kamere kroz variranje teksture 3D objekta. U svrhu uklju─Źenja iscrtavanja slika i promjene teksture objekta u cjevovod koristi se diferencijabilni prikaziva─Ź PyTorch3D. Detaljno su opisani parametri scene prikaziva─Źa i skup podataka. Dotaknuta je i tema konvolucijskih neuronskih mre┼ża i njihova primjena u detekciji objekata kao i EfficientNet u ulozi ekstraktora zna─Źajki. Na kraju rada prikazani su rezultati testiranja nau─Źenih modela sa promjenama tekstura na skupu za testiranje.The topic of this paper is to improve the results of depth estimation of an object from camera through the variation of the texture of a 3D object. A differentiable PyTorch3D renderer is used to include image rendering and variation of the texture of a object in the pipeline. The rendererÔÇÖs scene parameters and data set are described in detail. The topic of convolutional neural networks and their application in object detection as well as EfficientNet in feature extractor role were also touched upon. At the end of the paper, the results of testing the learned models with the change of texture on the testing set are presented

    Improving of Detection Results Through Variation of Object Appearance

    No full text
    Tema ovog rada je pobolj┼íanje rezultata procjene dubinske udaljenosti objekta od kamere kroz variranje teksture 3D objekta. U svrhu uklju─Źenja iscrtavanja slika i promjene teksture objekta u cjevovod koristi se diferencijabilni prikaziva─Ź PyTorch3D. Detaljno su opisani parametri scene prikaziva─Źa i skup podataka. Dotaknuta je i tema konvolucijskih neuronskih mre┼ża i njihova primjena u detekciji objekata kao i EfficientNet u ulozi ekstraktora zna─Źajki. Na kraju rada prikazani su rezultati testiranja nau─Źenih modela sa promjenama tekstura na skupu za testiranje.The topic of this paper is to improve the results of depth estimation of an object from camera through the variation of the texture of a 3D object. A differentiable PyTorch3D renderer is used to include image rendering and variation of the texture of a object in the pipeline. The rendererÔÇÖs scene parameters and data set are described in detail. The topic of convolutional neural networks and their application in object detection as well as EfficientNet in feature extractor role were also touched upon. At the end of the paper, the results of testing the learned models with the change of texture on the testing set are presented
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