Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for
  Lightweight Skin Lesion Classification Using Dermoscopic Images   Skin cancer is one of the most common types of malignancy, affecting a large
population and causing a heavy economic burden worldwide. Over the last few
years, computer-aided diagnosis has been rapidly developed and make great
progress in healthcare and medical practices due to the advances in artificial
intelligence. However, most studies in skin cancer detection keep pursuing high
prediction accuracies without considering the limitation of computing resources
on portable devices. In this case, knowledge distillation (KD) has been proven
as an efficient tool to help improve the adaptability of lightweight models
under limited resources, meanwhile keeping a high-level representation
capability. To bridge the gap, this study specifically proposes a novel method,
termed SSD-KD, that unifies diverse knowledge into a generic KD framework for
skin diseases classification. Our method models an intra-instance relational
feature representation and integrates it with existing KD research. A dual
relational knowledge distillation architecture is self-supervisedly trained
while the weighted softened outputs are also exploited to enable the student
model to capture richer knowledge from the teacher model. To demonstrate the
effectiveness of our method, we conduct experiments on ISIC 2019, a large-scale
open-accessed benchmark of skin diseases dermoscopic images. Experiments show
that our distilled lightweight model can achieve an accuracy as high as 85% for
the classification tasks of 8 different skin diseases with minimal parameters
and computing requirements. Ablation studies confirm the effectiveness of our
intra- and inter-instance relational knowledge integration strategy. Compared
with state-of-the-art knowledge distillation techniques, the proposed method
demonstrates improved performances for multi-diseases classification on the
large-scale dermoscopy database.

22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
19
The categories of this paper are: eess
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg