Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis
- URL: http://arxiv.org/abs/2307.04541v2
- Date: Fri, 21 Jul 2023 05:08:44 GMT
- Title: Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis
- Authors: Mingyuan Liu, Lu Xu, Jicong Zhang
- Abstract summary: Open set recognition (OSR) states that categories unseen in training could appear in testing.
OSR requires an algorithm to not only correctly classify known classes, but also recognize unknown classes and forward them to experts for further diagnosis.
We propose Open Margin Cosine Loss (OMCL) unifying two mechanisms. The former, called Margin Loss with Adaptive Scale (MLAS), introduces angular margin for reinforcing intra-class compactness and inter-class separability.
The latter, called Open-Space Suppression (OSS), opens the classifier by recognizing sparse embedding space as unknowns using proposed feature space descriptors.
- Score: 8.131130865777346
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fueled by deep learning, computer-aided diagnosis achieves huge advances.
However, out of controlled lab environments, algorithms could face multiple
challenges. Open set recognition (OSR), as an important one, states that
categories unseen in training could appear in testing. In medical fields, it
could derive from incompletely collected training datasets and the constantly
emerging new or rare diseases. OSR requires an algorithm to not only correctly
classify known classes, but also recognize unknown classes and forward them to
experts for further diagnosis. To tackle OSR, we assume that known classes
could densely occupy small parts of the embedding space and the remaining
sparse regions could be recognized as unknowns. Following it, we propose Open
Margin Cosine Loss (OMCL) unifying two mechanisms. The former, called Margin
Loss with Adaptive Scale (MLAS), introduces angular margin for reinforcing
intra-class compactness and inter-class separability, together with an adaptive
scaling factor to strengthen the generalization capacity. The latter, called
Open-Space Suppression (OSS), opens the classifier by recognizing sparse
embedding space as unknowns using proposed feature space descriptors. Besides,
since medical OSR is still a nascent field, two publicly available benchmark
datasets are proposed for comparison. Extensive ablation studies and feature
visualization demonstrate the effectiveness of each design. Compared with
state-of-the-art methods, MLAS achieves superior performances, measured by ACC,
AUROC, and OSCR.
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