@inproceedings{MTMT:34676556,
	title = {Assessing Conventional and Deep Learning-Based Approaches for Named Entity Recognition in Unstructured Hungarian Medical Reports},
	url = {https://m2.mtmt.hu/api/publication/34676556},
	author = {Bogacsovics, Gergő and Harangi, Balázs and Beregi-Kovács, Marcell and Kupás, Dávid and Lakatos, Róbert and Serbán, Norbert Dániel and Tiba, Attila and Tóth, János},
	booktitle = {22nd IEEE World Symposium on Applied Machine Intelligence and Informatics, SAMI 2024},
	doi = {10.1109/SAMI60510.2024.10432795},
	unique-id = {34676556},
	abstract = {In digital healthcare, much patient data is available in text format. The structuring of this data, according to standards, has yet to be widely used, including in Hungary, where it is available in unstructured form. To make these patient records easy to filter and search, they must be processed and structured. Using modern natural language processing and deep learning techniques has resulted in effective systems for implementing such workflows. However, selecting appropriate algorithms for specific text-processing tasks is still a challenging issue. This is due to the scarcity of benchmarks and the variety of architectures available. This article evaluates models for named entity recognition (NER) in digital medical reports written in Hungarian. We evaluate traditional, recurrent neural network, and transformer-based approaches for NER using a dataset comprising 801 positron emission tomography scans and annotated medical reports. The medical reports were annotated to cover six different entity classes and reviewed by clinical experts to ensure accuracy. We present a comprehensive assessment of various methods and provide insight into addressing NER problems in the case of low-resource languages such as Hungarian.},
	keywords = {positron emission tomography; natural language processing; Named entity recognition; deep-learning; medical-text record},
	year = {2024},
	pages = {77-82},
	orcid-numbers = {Harangi, Balázs/0000-0003-4405-2040}
}

@article{MTMT:34850523,
	title = {Distinguishing the Uterine Artery, the Ureter, and Nerves in Laparoscopic Surgical Images Using Ensembles of Binary Semantic Segmentation Networks},
	url = {https://m2.mtmt.hu/api/publication/34850523},
	author = {Serban, Norbert and Kupás, Dávid and Hajdu, András and Török, Péter and Harangi, Balázs},
	doi = {10.3390/s24092926},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {24},
	unique-id = {34850523},
	abstract = {Performing a minimally invasive surgery comes with a significant advantage regarding rehabilitating the patient after the operation. But it also causes difficulties, mainly for the surgeon or expert who performs the surgical intervention, since only visual information is available and they cannot use their tactile senses during keyhole surgeries. This is the case with laparoscopic hysterectomy since some organs are also difficult to distinguish based on visual information, making laparoscope-based hysterectomy challenging. In this paper, we propose a solution based on semantic segmentation, which can create pixel-accurate predictions of surgical images and differentiate the uterine arteries, ureters, and nerves. We trained three binary semantic segmentation models based on the U-Net architecture with the EfficientNet-b3 encoder; then, we developed two ensemble techniques that enhanced the segmentation performance. Our pixel-wise ensemble examines the segmentation map of the binary networks on the lowest level of pixels. The other algorithm developed is a region-based ensemble technique that takes this examination to a higher level and makes the ensemble based on every connected component detected by the binary segmentation networks. We also introduced and trained a classic multi-class semantic segmentation model as a reference and compared it to the ensemble-based approaches. We used 586 manually annotated images from 38 surgical videos for this research and published this dataset.},
	year = {2024},
	eissn = {1424-8220},
	pages = {1-12},
	orcid-numbers = {Serban, Norbert/0009-0000-6862-757X; Harangi, Balázs/0000-0003-4405-2040}
}

@article{MTMT:35088086,
	title = {Annotated Pap cell images and smear slices for cell classification},
	url = {https://m2.mtmt.hu/api/publication/35088086},
	author = {Kupás, Dávid and Hajdu, András and Kovács, Ilona and Hargitai, Zoltan and Szombathy, Zita and Harangi, Balázs},
	doi = {10.1038/s41597-024-03596-3},
	journal-iso = {SCI DATA},
	journal = {SCIENTIFIC DATA},
	volume = {11},
	unique-id = {35088086},
	abstract = {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 ( A nnotated PA p cell images and smear slices for C ell C lassification), 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.},
	year = {2024},
	eissn = {2052-4463},
	pages = {1-8},
	orcid-numbers = {Harangi, Balázs/0000-0003-4405-2040}
}

@inproceedings{MTMT:33116879,
	title = {Classification of Pap-smear cell images using deep convolutional neural network accelerated by hand-crafted features},
	url = {https://m2.mtmt.hu/api/publication/33116879},
	author = {Kupás, Dávid and Harangi, Balázs},
	booktitle = {2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)},
	doi = {10.1109/EMBC48229.2022.9871171},
	unique-id = {33116879},
	year = {2022},
	pages = {1452-1455},
	orcid-numbers = {Harangi, Balázs/0000-0003-4405-2040}
}

@inproceedings{MTMT:33098060,
	title = {Solving the problem of imbalanced dataset with synthetic image generation for cell classification using deep learning},
	url = {https://m2.mtmt.hu/api/publication/33098060},
	author = {Kupás, Dávid and Harangi, Balázs},
	booktitle = {2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)},
	doi = {10.1109/EMBC46164.2021.9631065},
	volume = {2021-January},
	unique-id = {33098060},
	year = {2021},
	pages = {2981-2984},
	orcid-numbers = {Harangi, Balázs/0000-0003-4405-2040}
}

@CONFERENCE{MTMT:32216643,
	title = {Deep learning-based cell classification in case of unbalanced dataset},
	url = {https://m2.mtmt.hu/api/publication/32216643},
	author = {Kupás, Dávid and Harangi, Balázs},
	booktitle = {Proceedings of CITDS2020 (Conference on Information Technology and Data Science)},
	unique-id = {32216643},
	year = {2020},
	pages = {116-117},
	orcid-numbers = {Harangi, Balázs/0000-0003-4405-2040}
}

@{MTMT:30881391,
	title = {Visualization of Fibroid in Laparoscopy Videos using Ultrasound Image Segmentation and Augmented Reality},
	url = {https://m2.mtmt.hu/api/publication/30881391},
	author = {Kupás, Dávid and Torok, Peter and Hajdu, András and Harangi, Balázs},
	booktitle = {2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)},
	doi = {10.1109/ISPA.2019.8868446},
	unique-id = {30881391},
	year = {2019},
	pages = {60-63},
	orcid-numbers = {Kupás, Dávid/0000-0003-4405-2040; Harangi, Balázs/0000-0003-4405-2040}
}

@inproceedings{MTMT:30881395,
	title = {Cell detection on digitized Pap smear images using ensemble of conventional image processing and deep learning techniques},
	url = {https://m2.mtmt.hu/api/publication/30881395},
	author = {Harangi, Balázs and Tóth, János and Bogacsovics, Gergő and Kupás, Dávid and Kovács, László and Hajdu, András},
	booktitle = {2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)},
	doi = {10.1109/ISPA.2019.8868683},
	unique-id = {30881395},
	year = {2019},
	pages = {38-42},
	orcid-numbers = {Harangi, Balázs/0000-0003-4405-2040}
}

@inproceedings{MTMT:3284309,
	title = {Decision support system for the diagnosis of neurological disorders based on gaze tracking},
	url = {https://m2.mtmt.hu/api/publication/3284309},
	author = {Kupás, Dávid and Harangi, Balázs and György, Czifra and Gábor, Andrássy},
	booktitle = {10th International Symposium on Image and Signal Processing and Analysis},
	doi = {10.1109/ISPA.2017.8073565},
	unique-id = {3284309},
	year = {2017},
	pages = {37-40},
	orcid-numbers = {Harangi, Balázs/0000-0003-4405-2040}
}