49 research outputs found
Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection
Visual artefacts of early diabetic retinopathy in retinal fundus images are
usually small in size, inconspicuous, and scattered all over retina. Detecting
diabetic retinopathy requires physicians to look at the whole image and fixate
on some specific regions to locate potential biomarkers of the disease.
Therefore, getting inspiration from ophthalmologist, we propose to combine
coarse-grained classifiers that detect discriminating features from the whole
images, with a recent breed of fine-grained classifiers that discover and pay
particular attention to pathologically significant regions. To evaluate the
performance of this proposed ensemble, we used publicly available EyePACS and
Messidor datasets. Extensive experimentation for binary, ternary and quaternary
classification shows that this ensemble largely outperforms individual image
classifiers as well as most of the published works in most training setups for
diabetic retinopathy detection. Furthermore, the performance of fine-grained
classifiers is found notably superior than coarse-grained image classifiers
encouraging the development of task-oriented fine-grained classifiers modelled
after specialist ophthalmologists.Comment: Pages 12, Figures
Learning Semantically Enhanced Feature for Fine-Grained Image Classification
We aim to provide a computationally cheap yet effective approach for
fine-grained image classification (FGIC) in this letter. Unlike previous
methods that rely on complex part localization modules, our approach learns
fine-grained features by enhancing the semantics of sub-features of a global
feature. Specifically, we first achieve the sub-feature semantic by arranging
feature channels of a CNN into different groups through channel permutation.
Meanwhile, to enhance the discriminability of sub-features, the groups are
guided to be activated on object parts with strong discriminability by a
weighted combination regularization. Our approach is parameter parsimonious and
can be easily integrated into the backbone model as a plug-and-play module for
end-to-end training with only image-level supervision. Experiments verified the
effectiveness of our approach and validated its comparable performance to the
state-of-the-art methods. Code is available at https://github.com/cswluo/SEFComment: Accepted by IEEE Signal Processing Letters. 5 pages, 4 figures, 4
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