Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rethink Transfer Learning in Medical Image Classification   Transfer learning (TL) with deep convolutional neural networks (DCNNs) is
crucial for modern medical image classification (MIC). However, the current
practice of finetuning the entire pretrained model is puzzling, as most MIC
tasks rely only on low- to mid-level features that are learned by up to mid
layers of DCNNs. To resolve the puzzle, we perform careful empirical
comparisons of several existing deep and shallow models, and propose a novel
truncated TL method that consistently leads to comparable or superior
performance and compact models on two MIC tasks. Our results highlight the
importance of transferring the right level of pretrained visual features
commensurate with the intrinsic complexity of the task.

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The categories of this paper are: solv-int
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga