Visual Saliency Estimation and Its Applications

The human visual system can automatically emphasize some parts of the image and ignore the other parts when seeing an image or a scene. Visual Saliency Estimation (VSE) aims to imitate this functionality of the human visual system to estimate the degree of human attention attracted by different image regions and locate the salient object. The study of VSE will help us explore the way human visual systems extract objects from an image. It has wide applications, such as robot navigation, video surveillance, object tracking, self-driving, etc. The current VSE approaches on natural images models generic visual stimuli based on lower-level image features, e.g., locations, local/global contrast, and feature correlation. However, existing models still suffered from some drawbacks. First, these methods fail in the cases when the objects are near the image borders. Second, due to imperfect model assumptions, many methods cannot achieve good results when the images have complicated backgrounds. In this work, I focuses on solving these challenges on the natural images by proposing a new framework with more robust task-related priors, and I apply the framework to low-quality biomedical images. The new framework formulates VSE on natural images as a quadratic program (QP) problem. It proposes an adaptive center-based bias hypothesis to replace the most common image center-based center-bias, which is much more robust even when the objects are far away from the image center. Second, it models a new smoothness term to force similar color having similar saliency statistics, which is more robust than that based on region dissimilarity when the image has a complicated background or low contrast. The new approach achieves the best performance among 11 latest methods on three public datasets. Three approaches based on the framework by integrating both high-level domain-knowledge and robust low-level saliency assumptions are utilized to imitate the radiologists\u27 attention to detect breast tumors from breast ultrasound images

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care