Summary of A Data-driven Guided Decoding Mechanism For Diagnostic Captioning, by Panagiotis Kaliosis et al.
A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning
by Panagiotis Kaliosis, John Pavlopoulos, Foivos Charalampakos, Georgios Moschovis, Ion Androutsopoulos
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Diagnostic Captioning (DC) approach is introduced, generating a diagnostic text from medical images like X-rays and MRIs. This draft-like text can assist clinicians by providing an initial patient condition estimation, speeding up the diagnostic process while ensuring safety. The accuracy of this generated text relies heavily on how well key medical conditions depicted in the images are expressed. A new guided decoding method is proposed, incorporating existing tags capturing image conditions into the beam search of the DC process. This method is evaluated on two medical datasets using four DC systems ranging from generic CNN-RNN models to pre-trained Large Language Models, which can also be used for few- and zero-shot learning scenarios. The proposed mechanism improves performance in most cases across all evaluation measures. The open-source implementation is available at https://github.com/nlpaueb/dmmcs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Doctors use images like X-rays to diagnose patients. A new way of generating text from these images can help doctors by giving them an idea of the patient’s condition right away, which speeds up and makes the diagnostic process safer. This method uses existing information about what’s in the image to make the generated text more accurate. |
Keywords
» Artificial intelligence » Cnn » Rnn » Zero shot
