Description
Navigate to https://medpix.nlm.nih.gov/casebydiagnosis (Links to an external site.) and choose a case that interests you. Write a report that reviews the pathology associated with your chosen case and describes the normal anatomy and physiology. Explain the particular modality of the medical imaging used in your case’s diagnosis and point out its strengths and weaknesses in comparison to other methods and testing procedures. Discuss the prevalence of the problem and how much it costs to treat it annually.
Propose an image processing strategy to aid or replace the visual evaluation of the medical image. Your strategy should consider how images are acquired in a clinical setting and must account for untrained personnel, individual variability, and poor/inconsistent image quality. Discuss the potential for type I and type II errors and propose a standard procedure or calibration technique that could avoid these errors, if applicable. Finally, discuss how you might simulate the images from your case and provide training opportunities for future physicians.
Side comment: when taken to the external site, you can pick any image from the website, from A-Z no matter what image.
Sample Solution
DynamicâThe expense of gaining preparing information cases for acceptance of AI models is one of the principle worries in certifiable issues. The web is a complete hotspot for some kinds of information which can be utilized for AI assignments. Be that as it may, the circulated and dynamic nature of web directs the utilization of arrangements which can deal with these attributes. In this paper, we present a programmed topical information obtaining strategy from the web. We propose a novel kind of topical crawlers that utilization a half and half connection setting extraction technique for topical creeping to gain on-point site pages with least data transfer capacity use and with the most reduced expense. The topical crawlers which utilize the new connection setting extraction technique which is called Block Text Window (BTW), joins content window strategy with a square based strategy and conquers difficulties of every one of these strategies utilizing the upsides of the other one. Exploratory outcomes show the transcendence of BTW in examination with other programmed topical web information securing techniques dependent on standard measurements. Catchphrasesâcost-delicate learning, programmed web information obtaining, topical crawlers, interface setting. Presentation True AI issues have various difficulties during their procedure and different sorts of cost are related with each progression of arrangements proposed from the beginning as far as possible of this procedure. Utility or cost based AI attempts to consider these unmistakable expenses and analyze learning strategies dependent on more decently measurements. This methodology considers three primary advances particularly for the grouping task and each progression is related with its related expense during the procedure. These means are information procurement, model enlistment, and utilization of the incited model to characterize new information [1]. The expense of information obtaining is more dismissed than the others in many cost-touchy AI and arrangement inquires about. We will consider the expense of information obtaining from the web as proficient utilization of data transfer capacity which is accessible for topical crawlers. The web is one of the most exhaustive wellsprings of data for some, AI errands, for example, grouping and bunching. It contains various kinds of information which incorporate content, picture, and other media information. Anyway for obtaining these information from the enormous, dispersed, heterogeneous and dynamic web, we need techniques that naturally surf the website pages with productive utilization of accessible transfer speed and gather wanted information with predefined target subjects. Topical web crawlers are powerful devices to adapt to this test. They start from some beginning pages, called seed pages, separate connections of these pages and allocate a few scores to these connections dependent on the handiness of following these connections to reach to on-point pages. The primary issue in the plan of topical web crawlers is to make it feasible for them to foresee pertinence of pages which current connections will prompt. Perhaps the best asset of data in leading topical crawlers is connect setting of hyperlinks. As indicated by [2] setting of a hyperlink or connection setting is characterized as the terms that show up in the content around a hyperlink inside a Web page. The difficult inquiry in connect setting extraction is that how around of a hyperlink can be resolved. A human can without much of a stretch comprehend territories around a hyperlink from its connection setting, yet it’s anything but a simple assignment for a topical crawler. In this paper we proposed Block Text Window (BTW), a cross breed connect setting extraction strategy for topical web creeping. It uses the Vision-Based Page Segmentation (VIPS) calculation [3] for page division and as this calculation has a few deficiencies in extricating page squares precisely, BTW utilizes content window technique [2] on the content of page squares to remove connect settings all the more effectively. We have done experimental examinations on the exhibition of the proposed technique and contrasted it and the best existing methodologies dependent on various measurements. The remainder of this paper is composed as pursues: in the following segment we investigate related works, segment three depicts the proposed technique in detail, area four examine on exploratory outcomes, and the last segment contains the end. Related Works In light of the extent of this paper we explore three interrelated fields: cost-touchy information procurement, topical creeping, and connection setting extraction techniques. Cost-Sensitive Data Acquisition Numerous kinds of investigates have been done in fields, for example, dynamic learning and cost-touchy component choice and extraction that remain under the cost-delicate information procurement, from certain perspectives. The dynamic learning technique in [4] considers the expense of naming occurrences for the proposed recommender framework. The creators of [5] utilized a blend of profound and dynamic learning for picture order and attempt to limit the expense of allocating marks to the cases. As of late in [6] the scientists proposed a mix of classifier chains and punished calculated relapse which considers highlights cost. Liu et al. proposed a cost-delicate element choice strategy for imbalanced class issues [7]. Representation of connection setting extraction strategies by run of the mill tests including: utilizing entire page content, interface message, a DOM based technique, a Text Window strategy and a proper square based technique. In any case, there are not many examines that think about the expense of gathering cases. Weiss et al. [8] proposed an expense and utility-based assessment system that considers all means of an AI procedure. They allude to the expense of cases as the expense related with obtaining total preparing models. In light of the meanings of [8], the initiated model A has more utility than the instigated model B if and just if: ãCostã_total (A)
DynamicâThe expense of gaining preparing information cases for acceptance of AI models is one of the principle worries in certifiable issues. The web is a complete hotspot for some kinds of information which can be utilized for AI assignments. Be that as it may, the circulated and dynamic nature of web directs the utilization of arrangements which can deal with these attributes. In this paper, we present a programmed topical information obtaining strategy from the web. We propose a novel kind of topical crawlers that utilization a half and half connection setting extraction technique for topical creeping to gain on-point site pages with least data transfer capacity use and with the most reduced expense. The topical crawlers which utilize the new connection setting extraction technique which is called Block Text Window (BTW), joins content window strategy with a square based strategy and conquers difficulties of every one of these strategies utilizing the upsides of the other one. Exploratory outcomes show the transcendence of BTW in examination with other programmed topical web information securing techniques dependent on standard measurements. Catchphrasesâcost-delicate learning, programmed web information obtaining, topical crawlers, interface setting. Presentation True AI issues have various difficulties during their procedure and different sorts of cost are related with each progression of arrangements proposed from the beginning as far as possible of this procedure. Utility or cost based AI attempts to consider these unmistakable expenses and analyze learning strategies dependent on more decently measurements. This methodology considers three primary advances particularly for the grouping task and each progression is related with its related expense during the procedure. These means are information procurement, model enlistment, and utilization of the incited model to characterize new information [1]. The expense of information obtaining is more dismissed than the others in many cost-touchy AI and arrangement inquires about. We will consider the expense of information obtaining from the web as proficient utilization of data transfer capacity which is accessible for topical crawlers. The web is one of the most exhaustive wellsprings of data for some, AI errands, for example, grouping and bunching. It contains various kinds of information which incorporate content, picture, and other media information. Anyway for obtaining these information from the enormous, dispersed, heterogeneous and dynamic web, we need techniques that naturally surf the website pages with productive utilization of accessible transfer speed and gather wanted information with predefined target subjects. Topical web crawlers are powerful devices to adapt to this test. They start from some beginning pages, called seed pages, separate connections of these pages and allocate a few scores to these connections dependent on the handiness of following these connections to reach to on-point pages. The primary issue in the plan of topical web crawlers is to make it feasible for them to foresee pertinence of pages which current connections will prompt. Perhaps the best asset of data in leading topical crawlers is connect setting of hyperlinks. As indicated by [2] setting of a hyperlink or connection setting is characterized as the terms that show up in the content around a hyperlink inside a Web page. The difficult inquiry in connect setting extraction is that how around of a hyperlink can be resolved. A human can without much of a stretch comprehend territories around a hyperlink from its connection setting, yet it’s anything but a simple assignment for a topical crawler. In this paper we proposed Block Text Window (BTW), a cross breed connect setting extraction strategy for topical web creeping. It uses the Vision-Based Page Segmentation (VIPS) calculation [3] for page division and as this calculation has a few deficiencies in extricating page squares precisely, BTW utilizes content window technique [2] on the content of page squares to remove connect settings all the more effectively. We have done experimental examinations on the exhibition of the proposed technique and contrasted it and the best existing methodologies dependent on various measurements. The remainder of this paper is composed as pursues: in the following segment we investigate related works, segment three depicts the proposed technique in detail, area four examine on exploratory outcomes, and the last segment contains the end. Related Works In light of the extent of this paper we explore three interrelated fields: cost-touchy information procurement, topical creeping, and connection setting extraction techniques. Cost-Sensitive Data Acquisition Numerous kinds of investigates have been done in fields, for example, dynamic learning and cost-touchy component choice and extraction that remain under the cost-delicate information procurement, from certain perspectives. The dynamic learning technique in [4] considers the expense of naming occurrences for the proposed recommender framework. The creators of [5] utilized a blend of profound and dynamic learning for picture order and attempt to limit the expense of allocating marks to the cases. As of late in [6] the scientists proposed a mix of classifier chains and punished calculated relapse which considers highlights cost. Liu et al. proposed a cost-delicate element choice strategy for imbalanced class issues [7]. Representation of connection setting extraction strategies by run of the mill tests including: utilizing entire page content, interface message, a DOM based technique, a Text Window strategy and a proper square based technique. In any case, there are not many examines that think about the expense of gathering cases. Weiss et al. [8] proposed an expense and utility-based assessment system that considers all means of an AI procedure. They allude to the expense of cases as the expense related with obtaining total preparing models. In light of the meanings of [8], the initiated model A has more utility than the instigated model B if and just if: ãCostã_total (A)
