Szerző szerinti böngészés "Hajdu, Andras"

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    Developing diverse ensemble architectures for automatic brain tumor classification
    Bogacsovics, Gergo; Harangi, Balazs; Hajdu, Andras; Bogacsovics Gergo (2024-) (xxx); Harangi Balazs (2024-) (xxx); Hajdu András (1973-) (matematikus, informatikus); Adattudomány és Vizualizáció Tanszék -- 905; IK; Debreceni Egyetem
    Brain tumors pose a serious threat in our modern society, with a clear increase in global cases each year. Therefore, developing robust solutions that could automatically and reliably detect brain tumors in their early stages is of utmost importance. In our paper, we revisit the problem of building performant ensembles for clinical usage by maximizing the diversity of the member models during the training procedure. We present an improved, more robust, extended version of our framework and propose solutions that could be integrated into a Computer-Aided Diagnosis system to accurately classify some of the most common types of brain tumors: meningioma, glioma, and pituitary tumors. We show that the new framework based on the histogram loss can be seen as a natural extension of the former approach, as it also calculates the inner products of the latent vectors produced by each member to measure similarity, but at the same time, it also makes it possible to capture more complex patterns. We also present several variants of our framework to incorporate member models with varying dimensional feature vectors and to cope with imbalanced datasets. We evaluate our solutions on a clinically tested dataset of 3,064 T1-weighted contrast-enhanced magnetic resonance images and show that they greatly outperform other state-of-the-art approaches and the base architectures as well, achieving over 92% accuracy, 92% macro and weighted precision, 91% macro and 92% weighted F1 score, and over 90% macro and 92% weighted sensitivity. © The Author(s) 2024.
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    [S] Annotated Pap cell images and smear slices for cell classification
    Kupas, David; Hajdu, Andras; Kovacs, Ilona; Hargitai, Zoltan; Szombathy, Zita; Harangi, Balazs; Kupas Dávid (2024-) (xxx); Hajdu András (1973-) (matematikus, informatikus); Kovács Ilona (1965-) (patológus); Hargitai Zoltán (2024-) (xxx); Szombathy Zita (2024-) (xxx); Harangi Balázs (1986-) (programtervező matematikus); Adattudomány és Vizualizáció Tanszék -- 905; IK; Debreceni Egyetem; Debreceni Egyetem - Kenézy Kórház - Patológia Tanszék
    Machine learning-based systems have become instrumental in augmenting global efforts to combat cervical cancer. A burgeoning area of research focuses on leveraging artificial intelligence to enhance the cervical screening process, primarily through the exhaustive examination of Pap smears, traditionally reliant on the meticulous and labor-intensive analysis conducted by specialized experts. Despite the existence of some comprehensive and readily accessible datasets, the field is presently constrained by the limited volume of publicly available images and smears. As a remedy, our work unveils APACC (Annotated PAp cell images and smear slices for Cell Classification), a comprehensive dataset designed to bridge this gap. The APACC dataset features a remarkable array of images crucial for advancing research in this field. It comprises 103,675 annotated cell images, carefully extracted from 107 whole smears, which are further divided into 21,371 sub-regions for a more refined analysis. This dataset includes a vast number of cell images from conventional Pap smears and their specific locations on each smear, offering a valuable resource for in-depth investigation and study. © The Author(s) 2024.