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- W4384615843 endingPage "104482" @default.
- W4384615843 startingPage "104482" @default.
- W4384615843 abstract "Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the integration of multiple sources of data, such as images and text, has the potential to revolutionize the analysis and interpretation of biomedical data. However, it only caught researchers’ attention recently. To this end, there is a critical need to conduct a systematic review on this topic, identify the limitations of current work, and explore future directions. In this scoping review, we aim to provide a comprehensive overview of the current state of the field and identify key concepts, types of studies, and research gaps with a focus on biomedical images and texts joint learning, mainly because these two were the most commonly available data types in MDL research. This study reviewed the current uses of multimodal deep learning on five tasks: (1) Report generation, (2) Visual question answering, (3) Cross-modal retrieval, (4) Computer-aided diagnosis, and (5) Semantic segmentation. Our results highlight the diverse applications and potential of MDL and suggest directions for future research in the field. We hope our review will facilitate the collaboration of natural language processing (NLP) and medical imaging communities and support the next generation of decision-making and computer-assisted diagnostic system development." @default.
- W4384615843 created "2023-07-18" @default.
- W4384615843 creator A5000776140 @default.
- W4384615843 creator A5022030361 @default.
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- W4384615843 creator A5083081872 @default.
- W4384615843 creator A5085113833 @default.
- W4384615843 creator A5086529957 @default.
- W4384615843 date "2023-10-01" @default.
- W4384615843 modified "2023-10-17" @default.
- W4384615843 title "A scoping review on multimodal deep learning in biomedical images and texts" @default.
- W4384615843 cites W2064675550 @default.
- W4384615843 cites W2101105183 @default.
- W4384615843 cites W2101608218 @default.
- W4384615843 cites W2136655611 @default.
- W4384615843 cites W2152772232 @default.
- W4384615843 cites W2194775991 @default.
- W4384615843 cites W2735784619 @default.
- W4384615843 cites W2747623286 @default.
- W4384615843 cites W2891378911 @default.
- W4384615843 cites W2897980926 @default.
- W4384615843 cites W2901466771 @default.
- W4384615843 cites W2914203365 @default.
- W4384615843 cites W2951934944 @default.
- W4384615843 cites W2958089299 @default.
- W4384615843 cites W2959269630 @default.
- W4384615843 cites W2963150162 @default.
- W4384615843 cites W2963466845 @default.
- W4384615843 cites W2963954913 @default.
- W4384615843 cites W2963967185 @default.
- W4384615843 cites W2970105478 @default.
- W4384615843 cites W2971185145 @default.
- W4384615843 cites W2979525559 @default.
- W4384615843 cites W2981534530 @default.
- W4384615843 cites W2995225687 @default.
- W4384615843 cites W3011651912 @default.
- W4384615843 cites W3016218187 @default.
- W4384615843 cites W3037181498 @default.
- W4384615843 cites W3090420058 @default.
- W4384615843 cites W3091383840 @default.
- W4384615843 cites W3094595351 @default.
- W4384615843 cites W3099632249 @default.
- W4384615843 cites W3102363003 @default.
- W4384615843 cites W3103733301 @default.
- W4384615843 cites W3104609094 @default.
- W4384615843 cites W3112990870 @default.
- W4384615843 cites W3116423158 @default.
- W4384615843 cites W3128510757 @default.
- W4384615843 cites W3164654615 @default.
- W4384615843 cites W3164670515 @default.
- W4384615843 cites W3165058054 @default.
- W4384615843 cites W3166845084 @default.
- W4384615843 cites W3167252243 @default.
- W4384615843 cites W3168834895 @default.
- W4384615843 cites W3173237701 @default.
- W4384615843 cites W3175126073 @default.
- W4384615843 cites W3181101419 @default.
- W4384615843 cites W3183198896 @default.
- W4384615843 cites W3183848791 @default.
- W4384615843 cites W3188437627 @default.
- W4384615843 cites W3189279073 @default.
- W4384615843 cites W3198570286 @default.
- W4384615843 cites W3200936534 @default.
- W4384615843 cites W3201906559 @default.
- W4384615843 cites W3202198427 @default.
- W4384615843 cites W3203255640 @default.
- W4384615843 cites W3203635737 @default.
- W4384615843 cites W3204703315 @default.
- W4384615843 cites W3205595180 @default.
- W4384615843 cites W3205731527 @default.
- W4384615843 cites W3210070020 @default.
- W4384615843 cites W3210381198 @default.
- W4384615843 cites W3213921583 @default.
- W4384615843 cites W4220826373 @default.
- W4384615843 cites W4225128184 @default.
- W4384615843 cites W4226062754 @default.
- W4384615843 cites W4226070132 @default.
- W4384615843 cites W4283821415 @default.
- W4384615843 cites W4284711968 @default.
- W4384615843 cites W4284891501 @default.
- W4384615843 cites W4285108627 @default.
- W4384615843 cites W4285130434 @default.
- W4384615843 cites W4285236136 @default.
- W4384615843 cites W4285618474 @default.
- W4384615843 cites W4290996482 @default.
- W4384615843 cites W4293676823 @default.
- W4384615843 cites W4293918660 @default.
- W4384615843 cites W4295917908 @default.
- W4384615843 cites W4295938038 @default.
- W4384615843 cites W4295951577 @default.
- W4384615843 cites W4296027312 @default.
- W4384615843 cites W4308885870 @default.
- W4384615843 cites W4312266387 @default.
- W4384615843 cites W4312321989 @default.
- W4384615843 cites W4312533035 @default.
- W4384615843 cites W4313068071 @default.
- W4384615843 cites W4313307241 @default.