43,235 research outputs found

    On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

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    Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202

    Semantic Integration of Cervical Cancer Data Repositories to Facilitate Multicenter Association Studies: The ASSIST Approach

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    The current work addresses the unifi cation of Electronic Health Records related to cervical cancer into a single medical knowledge source, in the context of the EU-funded ASSIST research project. The project aims to facilitate the research for cervical precancer and cancer through a system that virtually unifi es multiple patient record repositories, physically located in different medical centers/hospitals, thus, increasing fl exibility by allowing the formation of study groups “on demand” and by recycling patient records in new studies. To this end, ASSIST uses semantic technologies to translate all medical entities (such as patient examination results, history, habits, genetic profi le) and represent them in a common form, encoded in the ASSIST Cervical Cancer Ontology. The current paper presents the knowledge elicitation approach followed, towards the defi nition and representation of the disease’s medical concepts and rules that constitute the basis for the ASSIST Cervical Cancer Ontology. The proposed approach constitutes a paradigm for semantic integration of heterogeneous clinical data that may be applicable to other biomedical application domains

    Highly accurate model for prediction of lung nodule malignancy with CT scans

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    Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX

    Gay and bisexual men’s perceptions of the donation and use of human biological samples for research: a qualitative study

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    Human biological samples (biosamples) are increasingly important in diagnosing, treating and measuring the prevalence of illnesses. For the gay and bisexual population, biosample research is particularly important for measuring the prevalence of human immunodeficiency virus (HIV). By determining people’s understandings of, and attitudes towards, the donation and use of biosamples, researchers can design studies to maximise acceptability and participation. In this study we examine gay and bisexual men’s attitudes towards donating biosamples for HIV research. Semi-structured telephone interviews were conducted with 46 gay and bisexual men aged between 18 and 63 recruited in commercial gay scene venues in two Scottish cities. Interview transcripts were analysed thematically using the framework approach. Most men interviewed seemed to have given little prior consideration to the issues. Participants were largely supportive of donating tissue for medical research purposes, and often favourable towards samples being stored, reused and shared. Support was often conditional, with common concerns related to: informed consent; the protection of anonymity and confidentiality; the right to withdraw from research; and ownership of samples. Many participants were in favour of the storage and reuse of samples, but expressed concerns related to data security and potential misuse of samples, particularly by commercial organisations. The sensitivity of tissue collection varied between tissue types and collection contexts. Blood, urine, semen and bowel tissue were commonly identified as sensitive, and donating saliva and as unlikely to cause discomfort. To our knowledge, this is the first in-depth study of gay and bisexual men’s attitudes towards donating biosamples for HIV research. While most men in this study were supportive of donating tissue for research, some clear areas of concern were identified. We suggest that these minority concerns should be accounted for to develop inclusive, evidence-informed research protocols that balance collective benefits with individual concerns

    Contested psychiatric ontology and feminist critique : 'female sexual dysfunction' and the diagnostic and statistical manual

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    In this article I discuss the emergence of Female Sexual Dysfunction (FSD) within American psychiatry and beyond in the postwar period, setting out what I believe to be important and suggestive questions neglected in existing scholarship. Tracing the nomenclature within successive editions of the American Psychiatric Association’s Diagnostic and Statistical Manual (DSM), I consider the reification of the term 'FSD', and the activism and scholarship that the rise of the category has occasioned. I suggest that analysis of FSD benefits from scrutiny of a wider range of sources (especially since the popular and scientific cross-pollinate). I explore the multiplicity of FSD that emerges when one examines this wider range, but I also underscore a reinscribing of anxieties about psychogenic aetiologies. I then argue that what makes the FSD case additionally interesting, over and above other conditions with a contested status, is the historically complex relationship between psychiatry and feminism that is at work in contemporary debates. I suggest that existing literature on FSD has not yet posed some of the most important and salient questions at stake in writing about women’s sexual problems in this period, and can only do this when the relationship between 'second-wave' feminism, 'post-feminism', psychiatry and psychoanalysis becomes part of the terrain to be analysed, rather than the medium through which analysis is conducted

    A review of a strategic roadmapping exercise to advance clinical translation of photoacoustic imaging: From current barriers to future adoption

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    Photoacoustic imaging (PAI), also referred to as optoacoustic imaging, has shown promise in early-stage clinical trials in a range of applications from inflammatory diseases to cancer. While the first PAI systems have recently received regulatory approvals, successful adoption of PAI technology into healthcare systems for clinical decision making must still overcome a range of barriers, from education and training to data acquisition and interpretation. The International Photoacoustic Standardisation Consortium (IPASC) undertook an community exercise in 2022 to identify and understand these barriers, then develop a roadmap of strategic plans to address them. Here, we outline the nature and scope of the barriers that were identified, along with short-, medium- and long-term community efforts required to overcome them, both within and beyond the IPASC group

    An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automati c Prognosis of MCI Patients

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    Alzheimer's disease (AD) and mild cognitive impairment (MCI), continue to be widely studied. While there is no consensus on whether MCIs actually "convert" to AD, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline image brain scan, of whether an MCI individual will convert to AD within a multi-year period following the initial clinical visit. This is in fact not a traditional supervised learning problem since, in ADNI, there are no definitive labeled examples of MCI conversion. Prior works have defined MCI subclasses based on whether or not clinical/cognitive scores such as CDR significantly change from baseline. There are concerns with these definitions, however, since e.g. most MCIs (and ADs) do not change from a baseline CDR=0.5, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative conversion definition, wherein an MCI patient is declared to be a converter if any of the patient's brain scans (at follow-up visits) are classified "AD" by an (accurately-designed) Control-AD classifier. This novel definition bootstraps the design of a second classifier, specifically trained to predict whether or not MCIs will convert. This second classifier thus predicts whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate this new definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations much more consistent with known AD brain region biomarkers. We also identify key prognostic region biomarkers, essential for accurately discriminating the converter and nonconverter groups
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