Automatic generation of medical reports from chest X-rays in Czech
| Název práce v češtině: | Automatické generování lékařských zpráv z rentgenových snímků hrudníku v češtině |
|---|---|
| Název v anglickém jazyce: | Automatic generation of medical reports from chest X-rays in Czech |
| Klíčová slova: | zpracování obrázků|generování přirozeného jazyka|lékařství|neuronové sítě |
| Klíčová slova anglicky: | image processing|natural language generation|medical|neural networks |
| Akademický rok vypsání: | 2021/2022 |
| Typ práce: | diplomová práce |
| Jazyk práce: | angličtina |
| Ústav: | Ústav formální a aplikované lingvistiky (32-UFAL) |
| Vedoucí / školitel: | Mgr. Rudolf Rosa, Ph.D. |
| Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 31.03.2022 |
| Datum zadání: | 31.03.2022 |
| Datum potvrzení stud. oddělením: | 12.04.2022 |
| Datum a čas obhajoby: | 13.09.2022 09:00 |
| Datum odevzdání elektronické podoby: | 20.07.2022 |
| Datum odevzdání tištěné podoby: | 25.07.2022 |
| Datum proběhlé obhajoby: | 13.09.2022 |
| Oponenti: | Mgr. Jindřich Libovický, Ph.D. |
| Zásady pro vypracování |
| The aim of the master thesis is the problem of automatic textual report generation for chest X-ray images. The input is one or more chest X-ray images and the output is an overall textual description (report) of the chest X-ray. This report should describe whether there are any diseases/abnormalities and, if so, they should be described in more detail.
The generation of textual descriptions will take place on the basis of neural networks and the final output will be in the Czech language. Some of openly available medical datasets will be used for training the network, but as they are not in the Czech language, they will be translated automatically using a freely available machine translation system. The final model created as a result of the thesis will be then evaluated in order to determine the final performance of the model. The goal is not to replace the doctor examination, as no guarantees can be made about the truthfulness of the model predictions. The thesis should rather produce an auxiliary tool that should help doctors examine X-rays. |
| Seznam odborné literatury |
| - POPEL, Martin, et al. Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals. Nature communications, 2020, 11.1: 1-15.
- JOHNSON, Alistair E. W., Tom POLLARD, Roger MARK, Seth BERKOWITZ and Steven HORNG, 2019. MIMIC-CXR Database (version 2.0.0). 2019. B.m.: physionet.org. Available at: doi:10.13026/C2JT1Q - DEMNER-FUSHMAN, Dina, et al. Preparing a collection of radiology examinations for distribution and retrieval. Journal of the American Medical Informatics Association, 2016, 23.2: 304-310. - ZENG, Xianhua, et al. Generating diagnostic report for medical image by high-middle-level visual information incorporation on double deep learning models. Computer Methods and Programs in Biomedicine, 2020, 197: 105700. - ALFARGHALY, Omar, et al. Automated radiology report generation using conditioned transformers. Informatics in Medicine Unlocked, 2021, 24: 100557. - AYESHA, Hareem, et al. Automatic medical image interpretation: State of the art and future directions. Pattern Recognition, 2021, 114: 107856. - RADFORD, Alec, et al. Language models are unsupervised multitask learners. OpenAI blog, 2019, 1.8: 9. - Tensorflow documentation: https://www.tensorflow.org/ - fastai documentation: https://docs.fast.ai/ - Hugging Face documentation: https://huggingface.co/ |