MetaArXiv Preprints | Open access journals lack image accessibility considerations in author guidelines

Abstract:  In recent decades, there has been a move to “open” research, that is, to increase the reach of research products to broader audiences. One component of open access is accessibility. Accessibility generally refers to data and other products being free and open to use by others, but accessibility also refers to considering and meeting the needs of people with disabilities for participation and inclusion. Ensuring that visual content is understandable is a major component of ensuring open access publications are accessible, and alt text is a common way to make inaccessible images and non-text content more accessible. Using image accessibility and alt text as a lens, our objective was to evaluate how open access journals incorporate disability accessibility as part of open access publishing. Using a random sample of 300 English language open access journals, we assessed author guidelines to understand image requirements for submissions and open access statements to understand how journals conceive of openness and accessibility. We found that most open access journals do not include disability accessibility elements in their guidelines to authors when submitting images as part of their scholarship. While over half the journals had required parameters for image submission, none of them required alt text. And while the majority of journals included the word ‘access’ or ‘accessibility’ in their open access statements, almost none included disability or inclusion related terms. Our results highlight the importance of guidelines. Our findings speak to the limits of some of the current frameworks of open access. Incorporating disability accessibility into open access has the potential to bridge existing information inequalities for people with disabilities – and to make sure that mandates for open research do not exacerbate those inequalities.

 

Checklist for Artificial Intelligence in Medical Imaging Reporting Adherence in Peer-Reviewed and Preprint Manuscripts With the Highest Altmetric Attention Scores: A Meta-Research Study – Umaseh Sivanesan, Kay Wu, Matthew D. F. McInnes, Kiret Dhindsa, Fateme Salehi, Christian B. van der Pol, 2022

 

 

Abstract:  Purpose: To establish reporting adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) in diagnostic accuracy AI studies with the highest Altmetric Attention Scores (AAS), and to compare completeness of reporting between peer-reviewed manuscripts and preprints. Methods: MEDLINE, EMBASE, arXiv, bioRxiv, and medRxiv were retrospectively searched for 100 diagnostic accuracy medical imaging AI studies in peer-reviewed journals and preprint platforms with the highest AAS since the release of CLAIM to June 24, 2021. Studies were evaluated for adherence to the 42-item CLAIM checklist with comparison between peer-reviewed manuscripts and preprints. The impact of additional factors was explored including body region, models on COVID-19 diagnosis and journal impact factor. Results: Median CLAIM adherence was 48% (20/42). The median CLAIM score of manuscripts published in peer-reviewed journals was higher than preprints, 57% (24/42) vs 40% (16/42), P < .0001. Chest radiology was the body region with the least complete reporting (P = .0352), with manuscripts on COVID-19 less complete than others (43% vs 54%, P = .0002). For studies published in peer-reviewed journals with an impact factor, the CLAIM score correlated with impact factor, rho = 0.43, P = .0040. Completeness of reporting based on CLAIM score had a positive correlation with a study’s AAS, rho = 0.68, P < .0001. Conclusions: Overall reporting adherence to CLAIM is low in imaging diagnostic accuracy AI studies with the highest AAS, with preprints reporting fewer study details than peer-reviewed manuscripts. Improved CLAIM adherence could promote adoption of AI into clinical practice and facilitate investigators building upon prior works.

XCIST – an open access x-ray/CT simulation toolkit – IOPscience

Abstract:  Objective: X-ray-based imaging modalities including mammography and computed tomography (CT) are widely used in cancer screening, diagnosis, staging, treatment planning, and therapy response monitoring. Over the past few decades, improvements to these modalities have resulted in substantially improved efficacy and efficiency, and substantially reduced radiation dose and cost. However, such improvements have evolved more slowly than would be ideal because lengthy preclinical and clinical evaluation is required. In many cases, new ideas cannot be evaluated due to the high cost of fabricating and testing prototypes. Wider availability of computer simulation tools could accelerate development of new imaging technologies. This paper introduces the development of a new open-access simulation environment for X-ray-based imaging. Approach: The X-ray-based Cancer Imaging Simulation Toolkit (XCIST) is developed in the context of cancer imaging, but can more broadly be applied. XCIST is physics-based, written in Python and C/C++, and currently consists of three major subsets: digital phantoms, the simulator itself (CatSim), and image reconstruction algorithms; planned future features include a fast dose-estimation tool and rigorous validation. To enable broad usage and to model and evaluate new technologies, XCIST is easily extendable by other researchers. To demonstrate XCIST’s ability to produce realistic images and to show the benefits of using XCIST for insight into the impact of separate physics effects on image quality, we present exemplary simulations by varying contributing factors such as noise and sampling. Main Results: The capabilities and flexibility of XCIST are demonstrated, showing easy applicability to specific simulation problems. Geometric and X-ray attenuation accuracy are shown, as well as XCIST’s ability to model multiple scanner and protocol parameters, and to attribute fundamental image quality characteristics to specific parameters. Significance: This work represents an important first step toward the goal of creating an open-access platform for simulating existing and emerging X-ray-based imaging systems.

Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility and usability

Abstract:  Background and aims: Publicly available databases containing colonoscopic imaging data are valuable resources for artificial intelligence (AI) research. Currently, little is known regarding the available number and content of these databases. This review aimed to describe the availability, accessibility and usability of publicly available colonoscopic imaging databases, focusing on polyp detection, polyp characterization and quality of colonoscopy. Methods: A systematic literature search was performed in MEDLINE and Embase to identify AI-studies describing publicly available colonoscopic imaging datasets published after 2010. Second, a targeted search using Google’s Dataset Search, Google Search, GitHub and Figshare was done to identify datasets directly. Datasets were included if they contained data about polyp detection, polyp characterization or quality of colonoscopy. To assess accessibility of datasets the following categories were defined: open access, open access with barriers and regulated access. To assess the potential usability of the included datasets, essential details of each dataset were extracted using a checklist derived from the CLAIM-checklist. Results: We identified 22 datasets with open access, 3 datasets open access with barriers and 15 datasets with regulated access. The 22 open access databases containing 19,463 images and 952 videos. Nineteen of these databases focused on polyp detection, localization and/or segmentation, six on polyp characterization and three on quality of colonoscopy. Only half of these databases have been used by other researcher to develop, train or benchmark their AI-system. Although technical details were in general well-reported, important details such as polyp and patient demographics and the annotation process were underreported in almost all databases. Conclusion: This review provides greater insight on public availability of colonoscopic imaging databases for AI-research. Incomplete reporting of important details limits the ability of researchers to assess the usability of the current databases.

How to search for images you can (legally) use for free – The Verge

“If you’re looking for an image that you can repurpose for one of your projects and aren’t able to take a photo yourself, there are a ton of free images you can use online without running into any copyright issues — you just have to know where to look.

Here, we’ll go over different places where you can search for free images on the web. It’s worth noting that when searching for free images, you’ll often come across the Creative Commons (CC) license that lets you use an image for free. But depending on the type of CC license an image has, there may be some limitations that require you to credit the original artist or prevent you from making modifications to the image….”

Growth of open content in 2021 – About JSTOR

“By combining rapidly expanding open and free primary sources with our continuously growing journal archives, we strengthen the depth of your patrons’ research and enhance the value of your investment in JSTOR.

We introduced new functionality and more diverse types of open content from publishers, libraries, archives, and museums, including more open images from Artstor and open primary source collections. These resources are complemented with essential Open Access scholarship and address the increased needs for remote teaching and learning….”

Millions of Images From Ebony and Jet Magazines Will Soon Be Accessible to All

“Ownership of the Johnson Publishing Company Archive, which includes the photographic archives of Ebony and Jet magazines, has been formally transferred jointly to the Smithsonian National Museum of African American History and Culture (NMAAHC) and the Getty Research Institute. Sold for $30 million in 2019, academics, archivists, and artists were reassured to learn that the vast trove would pass into the hands of institutions committed to preserving and facilitating public access to it after extended anxiety that it would be won at auction by private collectors….”

An Action Plan for Accessible Images: Practical Solutions for Publishers, Platforms, and Providers – The Scholarly Kitchen

“When it comes to delivering accessible scholarly publications — specifically, those with equal access to collections of images or the graphics that appear in books and journals — we are working with a complex network of shared responsibilities. The Justice Department’s recently updated guidelines on web compliance with the Americans with Disabilities Act (ADA) are a good demonstration of how each player in the content supply chain, from authors to producers to distributors, must do their part to make images within scholarly publications accessible to everyone. As our friend Todd Carpenter pointed out last week, alternative text (alt text) for images is becoming a critical component of the high-value content produced by scholarly publishers.

The scholarly communications supply chain is not alone, as content and service providers of all kinds are adapting workflows to enrich image metadata and deliver on accessibility requirements. Companies like Google, Twitter, and Microsoft, among hundreds of others, acknowledge that accessibility and inclusive publishing practices are part of doing business in today’s digital information economy (and mitigate legal liabilities). In addition to leveling the playing field for disabled readers, accessible images offer all users an improved experience — from better search and faster navigation, to enriching audio experiences for those using text-to-speech assistive technologies….”

Getty opens access to 30,000 images of black diaspora in UK and US | Photography | The Guardian

“A collection of almost 30,000 rarely seen images of the black diaspora in the UK and the US, dating from the 19th century to the present, has been launched as part of an educational initiative to raise awareness of the history of black people in the UK.

The Black History & Culture Collection includes more than 20 categories of images including politics, hair, education, female empowerment and LGBTQ+….”

Artvee

“In the last few years, several major museums and libraries have instituted an open access policy by designating most or all of the public domain art in their collections with a creative commons license making them available for use for any purpose with no restrictions attached.

We sort through and aggregate the best of these images in one location to make them easy to discover and download.

Some of our sources include….”

Montreal’s McCord Museum launches remarkable new open access online platform | Arts | thesuburban.com

“To mark its 100th anniversary, the McCord Museum is launching a new open access platform with bilingual descriptions of over 140,000 objects, photographs, and archival documents from its collections. The site also features approximately 130,000 royalty-free images that may be downloaded in the highest resolution available, free of charge, with no restrictions on their use.

Created to provide unparalleled access to the Museum’s collections, the project is a first for the institution. The new platform, whose content will be constantly updated, was launched with the support of the Azrieli Foundation and Canadian Heritage….”

An open-access accelerated adult equivalent of the ABCD Study neuroimaging dataset (a-ABCD) – ScienceDirect

Abstract:  As public access to longitudinal developmental datasets like the Adolescent Brain Cognitive Development StudySM (ABCD Study®) increases, so too does the need for resources to benchmark time-dependent effects. Scan-to-scan changes observed with repeated imaging may reflect development but may also reflect practice effects, day-to-day variability in psychological states, and/or measurement noise. Resources that allow disentangling these time-dependent effects will be useful in quantifying actual developmental change. We present an accelerated adult equivalent of the ABCD Study dataset (a-ABCD) using an identical imaging protocol to acquire magnetic resonance imaging (MRI) structural, diffusion-weighted, resting-state and task-based data from eight adults scanned five times over five weeks. We report on the task-based imaging data (n?=?7). In-scanner stop-signal (SST), monetary incentive delay (MID), and emotional n-back (EN-back) task behavioral performance did not change across sessions. Post-scan recognition memory for emotional n-back stimuli, however, did improve as participants became more familiar with the stimuli. Functional MRI analyses revealed that patterns of task-based activation reflecting inhibitory control in the SST, reward success in the MID task, and working memory in the EN-back task were more similar within individuals across repeated scan sessions than between individuals. Within-subject, activity was more consistent across sessions during the EN-back task than in the SST and MID task, demonstrating differences in fMRI data reliability as a function of task. The a-ABCD dataset provides a unique testbed for characterizing the reliability of brain function, structure, and behavior across imaging modalities in adulthood and benchmarking neurodevelopmental change observed in the open-access ABCD Study.

 

Dataset of Indian and Thai banknotes with annotations – ScienceDirect

Abstract:  Multinational banknote detection in real time environment is the open research problem for the research community. Several studies have been conducted for providing solution for fast and accurate recognition of banknotes, detection of counterfeit banknotes, and identification of damaged banknotes. The State-of art techniques like machine learning (ML) and deep learning (DL) are dominating the traditional methods of digital image processing technique used for banknote classification. The success of the ML or DL projects heavily depends on size and comprehensiveness of dataset used. The available datasets have the following limitations:

 1. The size of existing Indian dataset is insufficient to train ML or DL projects [1], [2].

 2. The existing dataset fail to cover all denomination classes [1].

 3. The existing dataset does not consists of latest denomination [3].

 4. As per the literature survey there is no public open access dataset is available for Thai banknotes.

To overcome all these limitations we have created a total 3000 image dataset of Indian and Thai banknotes which include 2000 images of Indian banknotes and 1000 images of Thai banknotes. Indian banknotes consist of old and new banknotes of 10, 20, 50, 100, 200, 500 and 2000 rupees and Thai banknotes consist of 20, 50, 100, 500 and 1000 Baht.

The Digital Brain Bank, an open access platform for post-mortem datasets | eLife

Abstract:  Post-mortem MRI provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy, and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes – Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank release includes twenty one distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen non-human primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab’s investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.

 

The Digital Brain Bank, an open access platform for post-mortem datasets | eLife

Abstract:  Post-mortem MRI provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy, and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes – Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank release includes twenty one distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen non-human primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab’s investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.