Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Radiology Report Generation with a Learned Knowledge Base and
  Multi-modal Alignment   In clinics, a radiology report is crucial for guiding a patient's treatment.
Unfortunately, report writing imposes a heavy burden on radiologists. To
effectively reduce such a burden, we hereby present an automatic, multi-modal
approach for report generation from chest x-ray. Our approach, motivated by the
observation that the descriptions in radiology reports are highly correlated
with the x-ray images, features two distinct modules: (i) Learned knowledge
base. To absorb the knowledge embedded in the above-mentioned correlation, we
automatically build a knowledge base based on textual embedding. (ii)
Multi-modal alignment. To promote the semantic alignment among reports, disease
labels and images, we explicitly utilize textual embedding to guide the
learning of the visual feature space. We evaluate the performance of the
proposed model using metrics from both natural language generation and clinic
efficacy on the public IU and MIMIC-CXR datasets. Our ablation study shows that
each module contributes to improving the quality of generated reports.
Furthermore, with the aid of both modules, our approach clearly outperforms
state-of-the-art methods.

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The categories of this paper are: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
3
The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: nlin