499 research outputs found
Tools of Trade of the Next Blue-Collar Job? Antecedents, Design Features, and Outcomes of Interactive Labeling Systems
Supervised machine learning is becoming increasingly popular - and so is the need for annotated training data. Such data often needs to be manually labeled by human workers, not unlikely to negatively impact the involved workforce. To alleviate this issue, a new information systems class has emerged - interactive labeling systems. However, this young, but rapidly growing field lacks guidance and structure regarding the design of such systems. Against this backdrop, this paper describes antecedents, design features, and outcomes of interactive labeling systems. We perform a systematic literature review, identifying 188 relevant articles. Our results are presented as a morphological box with 14 dimensions, which we evaluate using card sorting. By additionally offering this box as a web-based artifact, we provide actionable guidance for interactive labeling system development for scholars and practitioners. Lastly, we discuss imbalances in the article distribution of our morphological box and suggest future work directions
Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease
Parkinson's Disease (PD) is the second most common neurodegenerative disease
in humans. PD is characterized by the gradual loss of dopaminergic neurons in
the Substantia Nigra (SN). Counting the number of dopaminergic neurons in the
SN is one of the most important indexes in evaluating drug efficacy in PD
animal models. Currently, analyzing and quantifying dopaminergic neurons is
conducted manually by experts through analysis of digital pathology images
which is laborious, time-consuming, and highly subjective. As such, a reliable
and unbiased automated system is demanded for the quantification of
dopaminergic neurons in digital pathology images. Recent years have seen a
surge in adopting deep learning solutions in medical image processing. However,
developing high-performing deep learning models hinges on the availability of
large-scale, high-quality annotated data, which can be expensive to acquire,
especially in applications like digital pathology image analysis. To this end,
we propose an end-to-end deep learning framework based on self-supervised
learning for the segmentation and quantification of dopaminergic neurons in PD
animal models. To the best of our knowledge, this is the first deep learning
model that detects the cell body of dopaminergic neurons, counts the number of
dopaminergic neurons, and provides characteristics of individual dopaminergic
neurons as a numerical output. Extensive experiments demonstrate the
effectiveness of our model in quantifying neurons with high precision, which
can provide a faster turnaround for drug efficacy studies, better understanding
of dopaminergic neuronal health status, and unbiased results in PD pre-clinical
research. As part of our contributions, we also provide the first publicly
available dataset of histology digital images along with expert annotations for
the segmentation of TH-positive DA neuronal soma
Generating semantically enriched diagnostics for radiological images using machine learning
Development of Computer Aided Diagnostic (CAD) tools to aid radiologists in pathology detection and decision making relies considerably on manually annotated images. With the advancement of deep learning techniques for CAD development, these expert annotations no longer need to be hand-crafted, however, deep learning algorithms require large amounts of data in order to generalise well. One way in which to access large volumes of expert-annotated data is through radiological exams consisting of images and reports. Using past radiological exams obtained from hospital archiving systems has many advantages: they are expert annotations available in large quantities, covering a population-representative variety of pathologies, and they provide additional context to pathology diagnoses, such as anatomical location and severity. Learning to auto-generate such reports from images presents many challenges such as the difficulty in representing and generating long, unstructured textual information, accounting for spelling errors and repetition or redundancy, and the inconsistency across different annotators. In this thesis, the problem of learning to automate disease detection from radiological exams is approached from three directions. Firstly, a report generation model is developed such that it is conditioned on radiological image features. Secondly, a number of approaches are explored aimed at extracting diagnostic information from free-text reports. Finally, an alternative approach to image latent space learning from current state-of-the-art is developed that can be applied to accelerated image acquisition.Open Acces
Next Generation Reporting and Diagnostic Tools for Healthcare and Biomedical Applications
Ph.DDOCTOR OF PHILOSOPH
Recommended from our members
Transformer Models for Clinical Target Prediction using Pathology Report Text
Structured electronic health record (EHR) data are commonly incomplete and can lack diagnostic detail. Clinical reports, on the other hand, are typically comprehensive and contain a wealth of detailed medical information. Pathologists invest considerable time and specialized training to create information-rich pathology reports, but the necessary manual review of these reports for clinical or research use is a high barrier to their routine utilization. The automated extraction of clinical targets directly from pathology reports would allow for the structured aggregation of relevant patient data that are not currently routinely captured in the EHR. In this dissertation, I apply recently developed transformer models to predict clinical targets from cancer pathology report text.
In the first chapter, I present a pathology report corpus that I fully processed and made publicly available, and perform a proof-of-concept cancer type classification. In the second chapter, I discuss a set of cancer stage classification models that I fine-tune on the pathology report corpus and then externally validate on reports from Columbia University Irving Medical Center (CUIMC).
In the last chapter, I explore additional applications for this methodology, developing a generalizable model to classify prostate cancer reports into primary Gleason score categories, applying a transformer model to classify reports into diagnosis categories for a Barrett’s esophagus patient cohort in a low-data environment, and performing a proof-of-concept prediction of adverse drug events from 1D drug representations
Developing ChatGPT for Biology and Medicine: A Complete Review of Biomedical Question Answering
ChatGPT explores a strategic blueprint of question answering (QA) in
delivering medical diagnosis, treatment recommendations, and other healthcare
support. This is achieved through the increasing incorporation of medical
domain data via natural language processing (NLP) and multimodal paradigms. By
transitioning the distribution of text, images, videos, and other modalities
from the general domain to the medical domain, these techniques have expedited
the progress of medical domain question answering (MDQA). They bridge the gap
between human natural language and sophisticated medical domain knowledge or
expert manual annotations, handling large-scale, diverse, unbalanced, or even
unlabeled data analysis scenarios in medical contexts. Central to our focus is
the utilizing of language models and multimodal paradigms for medical question
answering, aiming to guide the research community in selecting appropriate
mechanisms for their specific medical research requirements. Specialized tasks
such as unimodal-related question answering, reading comprehension, reasoning,
diagnosis, relation extraction, probability modeling, and others, as well as
multimodal-related tasks like vision question answering, image caption,
cross-modal retrieval, report summarization, and generation, are discussed in
detail. Each section delves into the intricate specifics of the respective
method under consideration. This paper highlights the structures and
advancements of medical domain explorations against general domain methods,
emphasizing their applications across different tasks and datasets. It also
outlines current challenges and opportunities for future medical domain
research, paving the way for continued innovation and application in this
rapidly evolving field.Comment: 50 pages, 3 figures, 3 table
Recuperação de informação multimodal em repositórios de imagem médica
The proliferation of digital medical imaging modalities in hospitals and other
diagnostic facilities has created huge repositories of valuable data, often
not fully explored. Moreover, the past few years show a growing trend
of data production. As such, studying new ways to index, process and
retrieve medical images becomes an important subject to be addressed by
the wider community of radiologists, scientists and engineers. Content-based
image retrieval, which encompasses various methods, can exploit the visual
information of a medical imaging archive, and is known to be beneficial to
practitioners and researchers. However, the integration of the latest systems
for medical image retrieval into clinical workflows is still rare, and their
effectiveness still show room for improvement.
This thesis proposes solutions and methods for multimodal information
retrieval, in the context of medical imaging repositories. The major
contributions are a search engine for medical imaging studies supporting
multimodal queries in an extensible archive; a framework for automated
labeling of medical images for content discovery; and an assessment and
proposal of feature learning techniques for concept detection from medical
images, exhibiting greater potential than feature extraction algorithms that
were pertinently used in similar tasks. These contributions, each in their
own dimension, seek to narrow the scientific and technical gap towards
the development and adoption of novel multimodal medical image retrieval
systems, to ultimately become part of the workflows of medical practitioners,
teachers, and researchers in healthcare.A proliferação de modalidades de imagem médica digital, em hospitais,
clínicas e outros centros de diagnóstico, levou à criação de enormes
repositórios de dados, frequentemente não explorados na sua totalidade.
Além disso, os últimos anos revelam, claramente, uma tendência para o
crescimento da produção de dados. Portanto, torna-se importante estudar
novas maneiras de indexar, processar e recuperar imagens médicas, por
parte da comunidade alargada de radiologistas, cientistas e engenheiros. A
recuperação de imagens baseada em conteúdo, que envolve uma grande
variedade de métodos, permite a exploração da informação visual num
arquivo de imagem médica, o que traz benefícios para os médicos e
investigadores. Contudo, a integração destas soluções nos fluxos de trabalho
é ainda rara e a eficácia dos mais recentes sistemas de recuperação de
imagem médica pode ser melhorada.
A presente tese propõe soluções e métodos para recuperação de informação
multimodal, no contexto de repositórios de imagem médica. As contribuições
principais são as seguintes: um motor de pesquisa para estudos de imagem
médica com suporte a pesquisas multimodais num arquivo extensível; uma
estrutura para a anotação automática de imagens; e uma avaliação e
proposta de técnicas de representation learning para deteção automática de
conceitos em imagens médicas, exibindo maior potencial do que as técnicas
de extração de features visuais outrora pertinentes em tarefas semelhantes.
Estas contribuições procuram reduzir as dificuldades técnicas e científicas
para o desenvolvimento e adoção de sistemas modernos de recuperação de
imagem médica multimodal, de modo a que estes façam finalmente parte
das ferramentas típicas dos profissionais, professores e investigadores da área
da saúde.Programa Doutoral em Informátic
Deep Learning in Single-Cell Analysis
Single-cell technologies are revolutionizing the entire field of biology. The
large volumes of data generated by single-cell technologies are
high-dimensional, sparse, heterogeneous, and have complicated dependency
structures, making analyses using conventional machine learning approaches
challenging and impractical. In tackling these challenges, deep learning often
demonstrates superior performance compared to traditional machine learning
methods. In this work, we give a comprehensive survey on deep learning in
single-cell analysis. We first introduce background on single-cell technologies
and their development, as well as fundamental concepts of deep learning
including the most popular deep architectures. We present an overview of the
single-cell analytic pipeline pursued in research applications while noting
divergences due to data sources or specific applications. We then review seven
popular tasks spanning through different stages of the single-cell analysis
pipeline, including multimodal integration, imputation, clustering, spatial
domain identification, cell-type deconvolution, cell segmentation, and
cell-type annotation. Under each task, we describe the most recent developments
in classical and deep learning methods and discuss their advantages and
disadvantages. Deep learning tools and benchmark datasets are also summarized
for each task. Finally, we discuss the future directions and the most recent
challenges. This survey will serve as a reference for biologists and computer
scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature—at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset—consisting of over 30 000 articles with manually reviewed topics—was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development
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