Enhancing Perceptual Attributes with Bayesian Style Generation

Deep learning has brought an unprecedented progress in computer vision and significant advances have been made in predicting subjective properties inherent to visual data (e.g., memorability, aesthetic quality, evoked emotions, etc.). Recently, some research works have even proposed deep learning approaches to modify images such as to appropriately alter these properties. Following this research line, this paper introduces a novel deep learning framework for synthesizing images in order to enhance a predefined perceptual attribute. Our approach takes as input a natural image and exploits recent models for deep style transfer and generative adversarial networks to change its style in order to modify a specific high-level attribute. Differently from previous works focusing on enhancing a specific property of a visual content, we propose a general framework and demonstrate its effectiveness in two use cases, i.e. increasing image memorability and generating scary pictures. We evaluate the proposed approach on publicly available benchmarks, demonstrating its advantages over state of the art methods.Comment: ACCV-201

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Nature inspired meta-heuristic algorithms for deep learning: recent progress and novel perspective

Deep learning is presently attracting extra ordinary attention from both the industry and the academia. The application of deep learning in computer vision has recently gain popularity. The optimization of deep learning models through nature inspired algorithms is a subject of debate in computer science. The application areas of the hybrid of natured inspired algorithms and deep learning architecture includes: machine vision and learning, image processing, data science, autonomous vehicles, medical image analysis, biometrics, etc. In this paper, we present recent progress on the application of nature inspired algorithms in deep learning. The survey pointed out recent development issues, strengths, weaknesses and prospects for future research. A new taxonomy is created based on natured inspired algorithms for deep learning. The trend of the publications in this domain is depicted; it shows the research area is growing but slowly. The deep learning architectures not exploit by the nature inspired algorithms for optimization are unveiled. We believed that the survey can facilitate synergy between the nature inspired algorithms and deep learning research communities. As such, massive attention can be expected in a near future