Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities

Institutional change and professional practices: The case of French doctoral education

Based on empirical research on the effect of doctoral schools in French university, this paper analyses under which conditions the implementation of a new institution impacts work practices i.e. the ways by which individuals and collective actors perform their activity. It focuses on the micro-practices of actors, in order to shed new light on the micro-level works which put the new institution into action. The paper contributes to existing theory in three different ways. First, it shows that institutional change does not generate new practices per se. Institutional change impacts work practices if the pre-existing practices are close to the new desired norm as promoted by the new institution. It thus emphasises proximity as a main mechanism of new practice diffusion, when actors are interdependent on each other. Second, in professional contexts based on practices distant from the new desired norm, actors adopt the new institution and change their practices if they are able to solve unaddressed problems. Such a dynamic is mainly based on the creation of new organisational arrangements or tools which mediate and enable problem solving activities. Finally, it proposes a delayed and indirect effect of the introduction of the new institution. By generating new interactions among actors, the new institution creates opportunities for comparisons across professions, legitimating one amongst the several existing norms in the field. Comparison amongst actors or disciplines leads to some categorising themselves as deviants, who lose legitimacy and power in the organisation.professional practices, institution, institution change, university

Edge cross-section profile for colonoscopic object detection

Colorectal cancer is the second leading cause of cancer-related deaths, claiming close to 50,000 lives annually in the United States alone. Colonoscopy is an important screening tool that has contributed to a significant decline in colorectal cancer-related deaths. During colonoscopy, a tiny video camera at the tip of the endoscope generates a video signal of the internal mucosa of the human colon. The video data is displayed on a monitor for real-time diagnosis by the endoscopist. Despite the success of colonoscopy in lowering cancer-related deaths, a significant miss rate for detection of both large polyps and cancers is estimated around 4-12%. As a result, in recent years, many computer-aided object detection techniques have been developed with the ultimate goal to assist the endoscopist in lowering the polyp miss rate. Automatic object detection in recorded video data during colonoscopy is challenging due to the noisy nature of endoscopic images caused by camera motion, strong light reflections, the wide angle lens that cannot be automatically focused, and the location and appearance variations of objects within the colon. The unique characteristics of colonoscopy video require new image/video analysis techniques. The dissertation presents our investigation on edge cross-section profile (ECSP), a local appearance model, for colonoscopic object detection. We propose several methods to derive new features on ECSP from its surrounding region pixels, its first-order derivative profile, and its second-order derivative profile. These ECSP features describe discriminative patterns for different types of objects in colonoscopy. The new algorithms and software using the ECSP features can effectively detect three representative types of objects and extract their corresponding semantic unit in terms of both accuracy and analysis time. The main contributions of dissertation are summarized as follows. The dissertation presents 1) a new ECSP calculation method and feature-based ECSP method that extracts features on ECSP for object detection, 2) edgeless ECSP method that calculates ECSP without using edges, 3) part-based multi-derivative ECSP algorithm that segments ECSP, its 1st - order and its 2nd - order derivative functions into parts and models each part using the method that is suitable to that part, 4) ECSP based algorithms for detecting three representative types of colonoscopic objects including appendiceal orifices, endoscopes during retroflexion operations, and polyps and extracting videos or segmented shots containing these objects as semantic units, and 5) a software package that implements these techniques and provides meaningful visual feedback of the detected results to the endoscopist. Ideally, we would like the software to provide feedback to the endoscopist before the next video frame becomes available and to process video data at the rate in which the data are captured (typically at about 30 frames per second (fps)). This real-time requirement is difficult to achieve using today\u27s affordable off-the-shelf workstations. We aim for achieving near real-time performance where the analysis and feedback complete at the rate of at least 1 fps. The dissertation has the following broad impacts. Firstly, the performance study shows that our proposed ECSP based techniques are promising both in terms of the detection rate and execution time for detecting the appearance of the three aforementioned types of objects in colonoscopy video. Our ECSP based techniques can be extended to both detect other types of colonoscopic objects such as diverticula, lumen and vessel, and analyze other endoscopy procedures, such as laparoscopy, upper gastrointestinal endoscopy, wireless capsule endoscopy and EGD. Secondly, to our best knowledge, our polyp detection system is the only computer-aided system that can warn the endoscopist the appearance of polyps in near real time. Our retroflexion detection system is also the first computer-aided system that can detect retroflexion in near real-time. Retroflexion is a maneuver used by the endoscopist to inspect the colon area that is hard to reach. The use of our system in future clinical trials may contribute to the decline in the polyp miss rate during live colonoscopy. Our system may be used as a training platform for novice endoscopists. Lastly, the automatic documentation of detected semantic units of colonoscopic objects can be helpful to discover unknown patterns of colorectal cancers or new diseases and used as educational resources for endoscopic research

DeepRoom: A Deep Learning Rating System for Photography

This thesis explores integrating deep learning techniques into photography, aiming to automate the identification of good images within large datasets. The primary focus is developing a deep learning-based system called DeepRoom that rates and evaluates photographs based on photography-specific technical criteria. To accomplish this, the research methodology encompasses qualitative research alongside developing a system prototype. A section overviews deep learning, photography, and related work and emphasizes its relevance to the research objectives. Implementation details include describing development tools and processes employed to construct the deep learning models and curate the dataset. These models' performance is assessed in the following evaluation phase, and a comparative analysis is conducted against existing software solutions. Encouraging results are observed, particularly in object detection and exposure classification, while identifying areas for improvement, such as refining the blurry and skewed horizon models. In conclusion, this research highlights the contributions of DeepRoom and proposes future work, including dataset expansion and model refinement, to enhance its capabilities further

A Vowel Analysis of the Northwestern University-Children\u27s Perception of Speech Evaluation Tool

In an analysis of the speech perception evaluation tool, the Northwestern University – Children’s Perception of Speech test, the goal was to determine whether the foil words and the target word were phonemically balanced across each page of test Book A, as it corresponds to the target words presented in Test Form 1 and Test Form 2 independently. Based on vowel sounds alone, variation exists in the vowels that appear on a test page on the majority of pages. The corresponding formant frequencies, at all three resonance levels for both the average adult male speaker and the average adult female speaker, revealed that the target word could be easily distinguished from the foil words on the premise of percent differences calculated between the formants of the target vowel and the foil vowels. For the population of children with hearing impairments, especially those with limited or no access to the high frequencies, the NU-CHIPS evaluation tool may not be the best indicator of the child’s speech perception ability due to significant vowel variations

Finding What You Need, and Knowing What You Can Find: Digital Tools for Palaeographers in Musicology and Beyond

This chapter examines three projects that provide musicologists with a range of resources for managing and exploring their materials: DIAMM (Digital Image Archive of Medieval Music), CMME (Computerized Mensural Music Editing) and the software Gamera. Since 1998, DIAMM has been enhancing research of scholars worldwide by providing them with the best possible quality of digital images. In some cases these images are now the only access that scholars are permitted, since the original documents are lost or considered too fragile for further handling. For many sources, however, simply creating a very high-resolution image is not enough: sources are often damaged by age, misuse (usually Medieval ‘vandalism’), or poor conservation. To deal with damaged materials the project has developed methods of digital restoration using mainstream commercial software, which has revealed lost data in a wide variety of sources. The project also uses light sources ranging from ultraviolet to infrared in order to obtain better readings of erasures or material lost by heat or water damage. The ethics of digital restoration are discussed, as well as the concerns of the document holders. CMME and a database of musical sources and editions, provides scholars with a tool for making fluid editions and diplomatic transcriptions: without the need for a single fixed visual form on a printed page, a computerized edition system can utilize one editor’s transcription to create any number of visual forms and variant versions. Gamera, a toolkit for building document image recognition systems created by Ichiro Fujinaga is a broad recognition engine that grew out of music recognition, which can be adapted and developed to perform a number of tasks on both music and non-musical materials. Its application to several projects is discussed