Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification

In this work, we propose a deep multi-deformation aware attention learning (MDAL) architecture comprising of multi-scale committee of attention (MSCA) and fine-grained feature induced attention (FGIA) modules to classify multi-target multi-class defects in concrete structures found in civil infrastructures. The MDAL network is composed of interleaved MSCA and FGIA modules to encode crucial fine-grained deformation-aware information from concrete images. The novel attention mechanism is able to localize specific defect regions within an image and extracts crucial discriminative information in multi-scale fashion ranging from coarser to finer features without using any preprocessing step, such as region-of-interest selection or denoising. Our proposed attention mechanism enables the MDAL architecture to automatically classify multiple overlapping defect classes present in the concrete images and leads to an end-to-end trainable deep network. Experimental results on three large concrete defect datasets and ablation studies show that our MDAL network outperforms the current state-of-the-art methodologies significantly

Perturbed Composite Attention Model for Macular Optical Coherence Tomography Image Classification

In this article, we propose a deep architecture stemming from a perturbed composite attention mechanism with the following two novel attention modules: Multilevel perturbed spatial attention (MPSA) and multidimension attention (MDA) for macular optical coherence tomography (OCT) image (scan) classification. MPSA is designed by adding positive perturbations to the attention layers, thereby amplifying both the salient regions of input images and discriminative features obtained from intermediate layers of the network. On the other hand, the MDA encodes the normalized interdependency of spatial information among various channels of the extracted feature maps. The perturbed composite attention mechanism enables the new architecture to automatically extract relevant diagnostic features at different levels of feature representation resulting in the superior classification of macular diseases such as age-related macular degeneration (AMD), diabetic macular edema (DME), and choroidal neovascularization (CNV). The proposed end-to-end trainable architecture does not require preprocessing steps, such as region of interest extraction, denoising, and retinal flattening, making the network more robust and fully automatic. Experimental results on three macular OCT datasets and ablation studies show that our proposed network outperforms the current state-of-the-art methodologies