Out-of-Distribution Detection for Dermoscopic Image Classification
- URL: http://arxiv.org/abs/2104.07819v2
- Date: Mon, 19 Apr 2021 05:47:57 GMT
- Title: Out-of-Distribution Detection for Dermoscopic Image Classification
- Authors: Mohammadreza Mohseni, Jordan Yap, William Yolland, Majid Razmara, M
Stella Atkins
- Abstract summary: We develop a novel yet simple method to train neural networks, which enables them to classify in-distribution dermoscopic skin disease images.
We show that our BinaryHeads model not only does not hurt classification balanced accuracy when the data is imbalanced, but also consistently improves the balanced accuracy.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Medical image diagnosis can be achieved by deep neural networks, provided
there is enough varied training data for each disease class. However, a
hitherto unknown disease class not encountered during training will inevitably
be misclassified, even if predicted with low probability. This problem is
especially important for medical image diagnosis, when an image of a hitherto
unknown disease is presented for diagnosis, especially when the images come
from the same image domain, such as dermoscopic skin images.
Current out-of-distribution detection algorithms act unfairly when the
in-distribution classes are imbalanced, by favouring the most numerous disease
in the training sets. This could lead to false diagnoses for rare cases which
are often medically important. We developed a novel yet simple method to train
neural networks, which enables them to classify in-distribution dermoscopic
skin disease images and also detect novel diseases from dermoscopic images at
test time. We show that our BinaryHeads model not only does not hurt
classification balanced accuracy when the data is imbalanced, but also
consistently improves the balanced accuracy. We also introduce an important
method to investigate the effectiveness of out-of-distribution detection
methods based on presence of varying amounts of out-of-distribution data, which
may arise in real-world settings.
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