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

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study

PadChest: A large chest x-ray image dataset with multi-label annotated reports

We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/

Microenvironmental control of malignant growth

The tumor microenvironment (TME) comprises a complex milieu of different cell types, including cancer associated fibroblasts (CAFs) and immune cells, blood vessels, and the extracellular matrix. Through its interaction with cancer cells, it plays an essential role in cancer invasion and metastasis. The inherent complexity of the TME presents a challenge to study it within experimental model systems. It underscores the importance of complementing such research with observation from human tumor tissues, wherein this intricate complexity is preserved. In Paper IV, we introduce a new software designed to explore the Human Protein Atlas, an online database that includes image data on the protein expression across normal and cancerous tissues from immunohistochemically (IHC) stained tissues. In Paper I, we use this software to identify 12 novel proteins expressed in cancerassociated fibroblasts, four revealing connections to Rho-kinase signaling. We contrast their expression across various tumors and against normal tissue fibroblasts, uncovering expression variability among cancer types and confirm their similarities with the myofibroblastic phenotype. In Paper II, we explore the expression of the proteoglycan Decorin, abundantly present in normal connective tissue and having tumor inhibitory properties, showing its downregulation in the connective tissue surrounding tumors. In Paper III, based on our observations in Paper I of the connection of Rhosignaling in CAFs, we study the effects of knocking out the related RhoA in fibroblasts both in vitro and in vivo models. We demonstrate that the knockout fibroblasts compromise their tumor inhibitory capacity, enhancing cancer cell growth, migration, and metastasis. In Paper VI, we develop a new method for analyzing the extensive data within the Human Protein Atlas by developing a deep-learning-based image classifier. Utilizing a limited training image set, we classify all images available for the prostate, identifying 44 new markers of prostate basal cells. In Paper IV, we explore the influence of the TME on cancer cells by systematically analyzing 20 pancreatic cancer patient samples utilizing an IHC panel. We define shifts in cancer cell phenotype relative to tissue localization, including a transition to a more indolent cancer phenotype, an effect on cancer cell proliferation, and a tendency to normalize the cancer cell phenotype. In conclusion, we developed two new methods that enable us to study protein expression in normal and cancerous tissues by enhancing the capabilities of the HPA. We identified new markers of CAFs and revealed a connection to Rhosignaling. Knocking out the related RhoA in experimental systems resulted in the fibroblasts losing their cancer inhibitory capacity. Finally, we show the remarkable plasticity of cancer cells, demonstrating that their phenotype undergoes significant alterations based on their spatial localization within normal tissue

PHOC Descriptor Applied for Mammography Classification

This paper describes experiments with PHOC (Pyramid Histogram of Color) features descriptor in terms of capacity for representing features presented in breast radiograph (also known as mammography). Patches were taken from regions in digital mammographies, representing benign, cancerous, normal tissues and image’s background. The motivation is to evaluate the proposal in perspective of using it for execution in an inexpensive ordinary desktop computer in places located far from medical experts. The images were obtained from DDSM database and processed producing the feature-dataset used for training an Artificial Neural Network, the results were evaluated by analysis of the learning rate curve and ROC curves, besides these graphical analytical tools the confusion matrix and other quantitative metrics (TPR, FPR and Accuracy) were also extracted and analyzed. The average accuracy ≈ 0.8 and the other metrics extracted from results demonstrate that the proposal presents potential for further developments. At the best effort, PHOC was not found in literature for applications in mammographies such as it is proposed here

Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review

[EN] This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010-January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. 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