TY - CHAP AU - Bogacsovics, Gergő AU - Harangi, Balázs AU - Beregi-Kovács, Marcell AU - Kupás, Dávid AU - Lakatos, Róbert AU - Serbán, Norbert Dániel AU - Tiba, Attila AU - Tóth, János TI - Assessing Conventional and Deep Learning-Based Approaches for Named Entity Recognition in Unstructured Hungarian Medical Reports T2 - 22nd IEEE World Symposium on Applied Machine Intelligence and Informatics, SAMI 2024 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway (NJ) SN - 9798350317213 T3 - Proceedings - International Symposium on Applied Machine Intelligence and Informatics, SAMI, ISSN 2836-0834 PY - 2024 SP - 77 EP - 82 PG - 6 DO - 10.1109/SAMI60510.2024.10432795 UR - https://m2.mtmt.hu/api/publication/34676556 ID - 34676556 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Serban, Norbert AU - Kupás, Dávid AU - Hajdu, András AU - Török, Péter AU - Harangi, Balázs TI - Distinguishing the Uterine Artery, the Ureter, and Nerves in Laparoscopic Surgical Images Using Ensembles of Binary Semantic Segmentation Networks JF - SENSORS J2 - SENSORS-BASEL VL - 24 PY - 2024 IS - 9 SP - 1 EP - 12 PG - 12 SN - 1424-8220 DO - 10.3390/s24092926 UR - https://m2.mtmt.hu/api/publication/34850523 ID - 34850523 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Kupás, Dávid AU - Hajdu, András AU - Kovács, Ilona AU - Hargitai, Zoltan AU - Szombathy, Zita AU - Harangi, Balázs TI - Annotated Pap cell images and smear slices for cell classification JF - SCIENTIFIC DATA J2 - SCI DATA VL - 11 PY - 2024 IS - 1 SP - 1 EP - 8 PG - 8 SN - 2052-4463 DO - 10.1038/s41597-024-03596-3 UR - https://m2.mtmt.hu/api/publication/35088086 ID - 35088086 AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Kupás, Dávid AU - Harangi, Balázs ED - Riccardo, Barbieri TI - Classification of Pap-smear cell images using deep convolutional neural network accelerated by hand-crafted features T2 - 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway (NJ) SN - 9781728127828 PY - 2022 SP - 1452 EP - 1455 PG - 4 DO - 10.1109/EMBC48229.2022.9871171 UR - https://m2.mtmt.hu/api/publication/33116879 ID - 33116879 LA - English DB - MTMT ER - TY - CHAP AU - Kupás, Dávid AU - Harangi, Balázs ED - Roy, ES TI - Solving the problem of imbalanced dataset with synthetic image generation for cell classification using deep learning T2 - 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) VL - 2021-January PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway (NJ) SN - 9781728111797 PY - 2021 SP - 2981 EP - 2984 PG - 4 DO - 10.1109/EMBC46164.2021.9631065 UR - https://m2.mtmt.hu/api/publication/33098060 ID - 33098060 LA - English DB - MTMT ER - TY - CONF AU - Kupás, Dávid AU - Harangi, Balázs TI - Deep learning-based cell classification in case of unbalanced dataset T2 - Proceedings of CITDS2020 (Conference on Information Technology and Data Science) PY - 2020 SP - 116 EP - 117 PG - 2 UR - https://m2.mtmt.hu/api/publication/32216643 ID - 32216643 LA - English DB - MTMT ER - TY - CHAP AU - Kupás, Dávid AU - Torok, Peter AU - Hajdu, András AU - Harangi, Balázs ED - Tomislav, Petković TI - Visualization of Fibroid in Laparoscopy Videos using Ultrasound Image Segmentation and Augmented Reality T2 - 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Danvers (MA) SN - 9781728131405 PY - 2019 SP - 60 EP - 63 PG - 4 DO - 10.1109/ISPA.2019.8868446 UR - https://m2.mtmt.hu/api/publication/30881391 ID - 30881391 LA - English DB - MTMT ER - TY - CHAP AU - Harangi, Balázs AU - Tóth, János AU - Bogacsovics, Gergő AU - Kupás, Dávid AU - Kovács, László AU - Hajdu, András ED - Tomislav, Petković TI - Cell detection on digitized Pap smear images using ensemble of conventional image processing and deep learning techniques T2 - 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Danvers (MA) SN - 9781728131405 PY - 2019 SP - 38 EP - 42 PG - 5 DO - 10.1109/ISPA.2019.8868683 UR - https://m2.mtmt.hu/api/publication/30881395 ID - 30881395 N1 - ISSN:1845-5921 LA - English DB - MTMT ER - TY - CHAP AU - Kupás, Dávid AU - Harangi, Balázs AU - György, Czifra AU - Gábor, Andrássy ED - Stanislav, Kovačič ED - Sven, Lončarić ED - Matej, Kristan ED - Vitomir, Štruc ED - Mladen, Vučić TI - Decision support system for the diagnosis of neurological disorders based on gaze tracking T2 - 10th International Symposium on Image and Signal Processing and Analysis PB - IEEE Signal Processing Society CY - Piscataway (NJ) SN - 9781509040117 PY - 2017 SP - 37 EP - 40 PG - 4 DO - 10.1109/ISPA.2017.8073565 UR - https://m2.mtmt.hu/api/publication/3284309 ID - 3284309 LA - English DB - MTMT ER -