Rethinking Convolutional Semantic Segmentation Learning
This addresses a convergence issue in semantic segmentation for medical imaging, offering a method to reduce reliance on pre-trained models, though it appears incremental as it builds on existing architectures.
The paper tackles the problem of deep convolutional semantic segmentation (DCSS) not converging with random initialization, requiring pre-trained models, by proposing a joint cooperative end-to-end learning method that simultaneously learns segmentation and classification, eliminating the need for pre-training. It demonstrates effectiveness on a diabetic retinopathy dataset, achieving faster convergence and better accuracy.
Deep convolutional semantic segmentation (DCSS) learning doesn't converge to an optimal local minimum with random parameters initializations; a pre-trained model on the same domain becomes necessary to achieve convergence.In this work, we propose a joint cooperative end-to-end learning method for DCSS. It addresses many drawbacks with existing deep semantic segmentation learning; the proposed approach simultaneously learn both segmentation and classification; taking away the essential need of the pre-trained model for learning convergence. We present an improved inception based architecture with partial attention gating (PAG) over encoder information. The PAG also adds to achieve faster convergence and better accuracy for segmentation task. We will show the effectiveness of this learning on a diabetic retinopathy classification and segmentation dataset.