Search Results for author:
Found 87 papers, 31 papers with code
Automatic recognition of abdominal lymph nodes from clinical text
We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports.
Measuring the relative importance of full text sections for information retrieval from scientific literature.
In this work we establish the connection between the BM25 score of a query term appearing in a section of a full text document and the probability of that document being clicked or identified as relevant.
Decomposing Vision-based LLM Predictions for Auto-Evaluation with GPT-4
The volume of CT exams being done in the world has been rising every year, which has led to radiologist burn-out.
Rethinking Scientific Summarization Evaluation: Grounding Explainable Metrics on Facet-aware Benchmark
Our findings confirm that FM offers a more logical approach to evaluating scientific summaries.
Benchmarking Retrieval-Augmented Generation for Medicine
However, a RAG system can involve multiple flexible components, and there is a lack of best practices regarding the optimal RAG setting for various medical purposes.
AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learning
Clinical calculators play a vital role in healthcare by offering accurate evidence-based predictions for various purposes such as prognosis.
A survey of recent methods for addressing AI fairness and bias in biomedicine
Such biases can occur before, during, or after the development of AI models, making it critical to understand and address potential biases to enable the accurate and reliable application of AI models in clinical settings.
Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science
Intelligent agents powered by large language models (LLMs) have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines.
Harnessing PubMed User Query Logs for Post Hoc Explanations of Recommended Similar Articles
Our study suggests that repurposing user query logs of academic search engines can be a promising way to train state-of-the-art models for explaining literature recommendation.
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports
In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging.
Unmasking and Quantifying Racial Bias of Large Language Models in Medical Report Generation
Large language models like GPT-3. 5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting their utility in medical applications.
Quality of Answers of Generative Large Language Models vs Peer Patients for Interpreting Lab Test Results for Lay Patients: Evaluation Study
Lab results are often confusing and hard to understand.
PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge
PubTator 3. 0 (https://www. ncbi. nlm. nih. gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases, and chemicals.
Hidden Flaws Behind Expert-Level Accuracy of GPT-4 Vision in Medicine
Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks.
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation.
Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence.
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions.
Matching Patients to Clinical Trials with Large Language Models
Second, the aggregated trial-level TrialGPT scores are highly correlated with expert eligibility annotations.
PubMed and Beyond: Biomedical Literature Search in the Age of Artificial Intelligence
In response, we present a survey of literature search tools tailored to both general and specific information needs in biomedicine, with the objective of helping readers efficiently fulfill their information needs.
A scoping review on multimodal deep learning in biomedical images and texts
In this scoping review, we aim to provide a comprehensive overview of the current state of the field and identify key concepts, types of studies, and research gaps with a focus on biomedical images and texts joint learning, mainly because these two were the most commonly available data types in MDL research.
MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval
In response, we introduce MedCPT, a first-of-its-kind Contrastively Pre-trained Transformer model for zero-shot semantic IR in biomedicine.
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets
State-of-the-art methods were used primarily to train machine learning models on individual RE datasets, such as protein-protein interaction and chemical-induced disease relation.
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health
In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health.
Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports
Pre-filling a radiology report holds promise in mitigating reporting errors, and despite efforts in the literature to generate medical reports, there exists a lack of approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset.
Large language models in biomedical natural language processing: benchmarks, baselines, and recommendations
Biomedical literature is growing rapidly, making it challenging to curate and extract knowledge manually.
GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information
In this paper, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions.
LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search
On all queries, LADER can improve the performance of a dense retriever by 24%-37% relative NDCG@10 while not requiring additional training, and further performance improvement is expected from more logs.
Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
Results: Using baseline prompts, GPT-3. 5 and GPT-4 achieved relaxed F1 scores of 0. 634, 0. 804 for MTSamples, and 0. 301, 0. 593 for VAERS.
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning
Medical visual question answering (VQA) aims to answer clinically relevant questions regarding input medical images.
Bioformer: an efficient transformer language model for biomedical text mining
We thoroughly evaluated the performance of Bioformer as well as existing biomedical BERT models including BioBERT and PubMedBERT on 15 benchmark datasets of four different biomedical NLP tasks: named entity recognition, relation extraction, question answering and document classification.
AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning
Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering.
LitCovid in 2022: an information resource for the COVID-19 literature
LitCovid (https://www. ncbi. nlm. nih. gov/research/coronavirus/), first launched in February 2020, is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19.
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning
A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection.
Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework
The automatic assignment of species information to the corresponding genes in a research article is a critically important step in the gene normalization task, whereby a gene mention is normalized and linked to a database record or identifier by a text-mining algorithm.
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
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.
LitMC-BERT: transformer-based multi-label classification of biomedical literature with an application on COVID-19 literature curation
However, it has been a primary curation bottleneck due to the nature of the task and the rapid literature growth.
BioRED: A Rich Biomedical Relation Extraction Dataset
However, most existing benchmarking datasets for bio-medical RE only focus on relations of a single type (e. g., protein-protein interactions) at the sentence level, greatly limiting the development of RE systems in biomedicine.
Ranked #1 on
Named Entity Recognition (NER)
on BioRED
tmVar 3.0: an improved variant concept recognition and normalization tool
We have also processed the entire PubMed and PMC with tmVar3 and released its annotations on our FTP.
Universal Lymph Node Detection in T2 MRI using Neural Networks
Results: Experiments on 122 test T2 MRI volumes revealed that VFNet achieved a 51. 1% mAP and 78. 7% recall at 4 false positives (FP) per volume, while the one-stage model ensemble achieved a mAP of 52. 3% and sensitivity of 78. 7% at 4FP.
Radiology Text Analysis System (RadText): Architecture and Evaluation
Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads of radiologists and encourage precise diagnosis.
A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis
To evaluate our proposed method's utility and privacy loss, we apply our model on a medical report disease label classification task using two noisy challenging clinical text datasets.
Perceiving and Modeling Density is All You Need for Image Dehazing
However, due to the paradox caused by the variation of real captured haze and the fixed degradation parameters of the current networks, the generalization ability of recent dehazing methods on real-world hazy images is not ideal. To address the problem of modeling real-world haze degradation, we propose to solve this problem by perceiving and modeling density for uneven haze distribution.
Ranked #5 on
Image Dehazing
on Haze4k
Lymph Node Detection in T2 MRI with Transformers
Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases.
The overview of the NLM-Chem BioCreative VII track: full-text chemical identification and indexing in PubMed articles
The BioCreative NLM-Chem track calls for a community effort to fine-tune automated recognition of chemical names in biomedical literature.
SDWNet: A Straight Dilated Network with Wavelet Transformation for Image Deblurring
On this basis, we reduce the number of up-sampling and down-sampling and design a simple network structure.
Ranked #1 on
Image Deblurring
on RealBlur-R(trained on GoPro)
BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer
A biomedical relation statement is commonly expressed in multiple sentences and consists of many concepts, including gene, disease, chemical, and mutation.
Ranked #2 on
Relation Extraction
on BioRED
Multi-modal, multi-task, multi-attention (M3) deep learning detection of reticular pseudodrusen: towards automated and accessible classification of age-related macular degeneration
The objective was to develop and evaluate the performance of a novel 'M3' deep learning framework on RPD detection.
A Comprehensive Dictionary and Term Variation Analysis for COVID-19 and SARS-CoV-2
In this manuscript we present an extensive dictionary of terms used in the literature to refer to SARS-CoV-2 and COVID-19.
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)
The COVID-19 pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread.
PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology
In this paper, we propose PhenoTagger, a hybrid method that combines both dictionary and machine learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text.
Navigating the landscape of COVID-19 research through literature analysis: A bird's eye view
In this study we analyze the LitCovid collection, 13, 369 COVID-19 related articles found in PubMed as of May 15th, 2020 with the purpose of examining the landscape of literature and presenting it in a format that facilitates information navigation and understanding.
Predicting risk of late age-related macular degeneration using deep learning
By 2040, age-related macular degeneration (AMD) will affect approximately 288 million people worldwide.
COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature
(1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR.
An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining
Multi-task learning (MTL) has achieved remarkable success in natural language processing applications.
TeamTat: a collaborative text annotation tool
Manually annotated data is key to developing text-mining and information-extraction algorithms.
BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale
Capturing the semantics of related biological concepts, such as genes and mutations, is of significant importance to many research tasks in computational biology such as protein-protein interaction detection, gene-drug association prediction, and biomedical literature-based discovery.
Biomedical Mention Disambiguation using a Deep Learning Approach
The priority ordering rule-based approach demonstrated F1-scores of 71. 29% (micro-averaged) and 41. 19% (macro-averaged), while the new disambiguation method demonstrated F1-scores of 91. 94% (micro-averaged) and 85. 42% (macro-averaged), a very substantial increase.
Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records
For the post challenge, the performance of both Random Forest and the Encoder Network was improved; in particular, the correlation of the Encoder Network was improved by ~13%.
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation
When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report.
Ranked #7 on
Medical Object Detection
on DeepLesion
A deep learning approach for automated detection of geographic atrophy from color fundus photographs
Results: The deep learning models (GA detection, CGA detection from all eyes, and centrality detection from GA eyes) had AUC of 0. 933-0. 976, 0. 939-0. 976, and 0. 827-0. 888, respectively.
A self-attention based deep learning method for lesion attribute detection from CT reports
In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity.
Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning from Radiology Reports and Label Ontology
In radiologists' routine work, one major task is to read a medical image, e. g., a CT scan, find significant lesions, and describe them in the radiology report.
Fine-grained lesion annotation in CT images with knowledge mined from radiology reports
To address this problem, we define a set of 145 labels based on RadLex to describe a large variety of lesions in the DeepLesion dataset.
MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's thorax, but requiring specialized training for proper interpretation.
Exploring Semi-supervised Variational Autoencoders for Biomedical Relation Extraction
Experimental results show that our method effectively exploits the unlabeled data to improve the performance and reduce the dependence on labeled data.
A multi-task deep learning model for the classification of Age-related Macular Degeneration
Built on our previous work DeepSeeNet, we developed a novel deep learning model for automated classification of images into the 9-step scale.
Age-Related Macular Degeneration Classification
Classification
+2
DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs
DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale.
ML-Net: multi-label classification of biomedical texts with deep neural networks
Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems.
BioSentVec: creating sentence embeddings for biomedical texts
Sentence embeddings have become an essential part of today's natural language processing (NLP) systems, especially together advanced deep learning methods.
Ranked #1 on
Sentence Embeddings For Biomedical Texts
on MedSTS
(using extra training data)
SingleCite: Towards an improved Single Citation Search in PubMed
A search that is targeted at finding a specific document in databases is called a Single Citation search.
MeSH-based dataset for measuring the relevance of text retrieval
Creating simulated search environments has been of a significant interest in infor-mation retrieval, in both general and bio-medical search domains.
Personalized neural language models for real-world query auto completion
Query auto completion (QAC) systems are a standard part of search engines in industry, helping users formulate their query.
A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval
Publications in the life sciences are characterized by a large technical vocabulary, with many lexical and semantic variations for expressing the same concept.
Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models
Text mining the relations between chemicals and proteins is an increasingly important task.
TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays
Chest X-rays are one of the most common radiological examinations in daily clinical routines.
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
Negative and uncertain medical findings are frequent in radiology reports, but discriminating them from positive findings remains challenging for information extraction.
Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs
We describe a Deep Learning approach to modeling the relevance of a document{'}s text to a query, applied to biomedical literature.
BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations
The Precision Medicine Track in BioCre-ative VI aims to bring together the Bi-oNLP community for a novel challenge focused on mining the biomedical litera-ture in search of mutations and protein-protein interactions (PPI).
Deep learning for extracting protein-protein interactions from biomedical literature
State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information.
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases.
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents
Here we present a query-document similarity measure motivated by the Word Mover's Distance.
Challenges in clinical natural language processing for automated disorder normalization
Conclusion Disorder mentions in text from clinical narratives use a rich vocabulary that results in high term variation, which we believe to be one of the primary causes of reduced performance in clinical narrative.
