Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Secure and Robust Machine Learning for Healthcare: A Survey   Recent years have witnessed widespread adoption of machine learning (ML)/deep
learning (DL) techniques due to their superior performance for a variety of
healthcare applications ranging from the prediction of cardiac arrest from
one-dimensional heart signals to computer-aided diagnosis (CADx) using
multi-dimensional medical images. Notwithstanding the impressive performance of
ML/DL, there are still lingering doubts regarding the robustness of ML/DL in
healthcare settings (which is traditionally considered quite challenging due to
the myriad security and privacy issues involved), especially in light of recent
results that have shown that ML/DL are vulnerable to adversarial attacks. In
this paper, we present an overview of various application areas in healthcare
that leverage such techniques from security and privacy point of view and
present associated challenges. In addition, we present potential methods to
ensure secure and privacy-preserving ML for healthcare applications. Finally,
we provide insight into the current research challenges and promising
directions for future research.

18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat