Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Modal Learning Using Physicians Diagnostics for Optical Coherence
  Tomography Classification   In this paper, we propose a framework that incorporates experts diagnostics
and insights into the analysis of Optical Coherence Tomography (OCT) using
multi-modal learning. To demonstrate the effectiveness of this approach, we
create a medical diagnostic attribute dataset to improve disease classification
using OCT. Although there have been successful attempts to deploy machine
learning for disease classification in OCT, such methodologies lack the experts
insights. We argue that injecting ophthalmological assessments as another
supervision in a learning framework is of great importance for the machine
learning process to perform accurate and interpretable classification. We
demonstrate the proposed framework through comprehensive experiments that
compare the effectiveness of combining diagnostic attribute features with
latent visual representations and show that they surpass the state-of-the-art
approach. Finally, we analyze the proposed dual-stream architecture and provide
an insight that determine the components that contribute most to classification
performance.

2
The categories of this paper are: math
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg