Search Results for author:
Found 103 papers, 39 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.
EchoGen: Generating Conclusions from Echocardiogram Notes
Generating a summary from findings has been recently explored (Zhang et al., 2018, 2020) in note types such as radiology reports that typically have short length.
CMU’s IWSLT 2022 Dialect Speech Translation System
We use additional paired Modern Standard Arabic data (MSA) to directly improve the speech recognition (ASR) and machine translation (MT) components of our cascaded systems.
Learned Scanpaths Aid Blind Panoramic Video Quality Assessment
Panoramic videos have the advantage of providing an immersive and interactive viewing experience.
Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes
Data accuracy is essential for scientific research and policy development.
Evaluating GPT-4 with Vision on Detection of Radiological Findings on Chest Radiographs
The study examines the application of GPT-4V, a multi-modal large language model equipped with visual recognition, in detecting radiological findings from a set of 100 chest radiographs and suggests that GPT-4V is currently not ready for real-world diagnostic usage in interpreting chest radiographs.
An Empirical Study of Speech Language Models for Prompt-Conditioned Speech Synthesis
Speech language models (LMs) are promising for high-quality speech synthesis through in-context learning.
MSLM-S2ST: A Multitask Speech Language Model for Textless Speech-to-Speech Translation with Speaker Style Preservation
There have been emerging research interest and advances in speech-to-speech translation (S2ST), translating utterances from one language to another.
Deep learning with noisy labels in medical prediction problems: a scoping review
This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation.
OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification
Inspired by the Open Whisper-style Speech Model (OWSM) project, we propose OWSM-CTC, a novel encoder-only speech foundation model based on Connectionist Temporal Classification (CTC).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
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.
SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition
Existing speech language models typically utilize task-dependent prompt tokens to unify various speech tasks in a single model.
OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer
In this work, we aim to improve the performance and efficiency of OWSM without extra training data.
Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive Learning
Our proposed AI model utilizes supervised contrastive learning to minimize bias in CXR diagnosis.
Nonparametric Estimation via Variance-Reduced Sketching
In this paper, we introduce a new framework called Variance-Reduced Sketching (VRS), specifically designed to estimate density functions and nonparametric regression functions in higher dimensions with a reduced curse of dimensionality.
Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search
The proposed method can be trained effectively by combining a bias phrase index loss and special tokens to detect the bias phrases in the input speech data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications
On the PICO-Corpus, PICOX obtained higher recall and F1 scores than the baseline and improved the micro recall score from 56. 66 to 67. 33.
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.
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions
Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model's behavior and surpassing performance of task-specific models.
Ranked #1 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning
We show that further efficiency can be achieved with a vanilla HuBERT Base model, which can maintain 94% of XLS-R's performance with only 3% of the data, 4 GPUs, and limited trials.
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Pre-training speech models on large volumes of data has achieved remarkable success.
Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech
To achieve comprehensive coverage of diverse speech tasks and harness instruction tuning, we invite the community to collaborate and contribute, facilitating the dynamic growth of the benchmark.
Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks
We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation.
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?
This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension
The objective of this study is to devise a strategy that can improve the model's capability to recognize biomedical entities in scenarios of few-shot learning.
High-performance Data Management for Whole Slide Image Analysis in Digital Pathology
The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario ("CPU scenario"), and (2) a GPU-based deep learning framework scenario ("GPU scenario").
From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence
In 2020, the U. S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields.
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.
Classifying Crime Types using Judgment Documents from Social Media
Experiments show that the proposed method achieves state-of-the-art results on the present dataset.
An empirical study of using radiology reports and images to improve ICU mortality prediction
Background: The predictive Intensive Care Unit (ICU) scoring system plays an important role in ICU management because it predicts important outcomes, especially mortality.
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.
Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Compute
Our code and training optimizations make SSL feasible with only 8 GPUs, instead of the 32 used in the original work.
Tensor decomposition for minimization of E2E SLU model toward on-device processing
We reduce the model size by applying tensor decomposition to the Conformer and E-Branchformer architectures used in our E2E SLU models.
Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses
To tackle this problem, we propose a controlled text generation method that uses a novel contrastive learning strategy to encourage models to differentiate between generating likely and less likely outputs according to humans.
DPHuBERT: Joint Distillation and Pruning of Self-Supervised Speech Models
Knowledge distillation trains a small student model to mimic the behavior of a large teacher model.
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks
Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, including automatic speech recognition (ASR), speech translation (ST) and spoken language understanding (SLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge
In the track, we adopt a pipeline approach of ASR and NLU.
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge
Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Generative Modeling via Hierarchical Tensor Sketching
We propose a hierarchical tensor-network approach for approximating high-dimensional probability density via empirical distribution.
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community.
Learning a Room with the Occ-SDF Hybrid: Signed Distance Function Mingled with Occupancy Aids Scene Representation
We found that the color rendering loss results in optimization bias against low-intensity areas, causing gradient vanishing and leaving these areas unoptimized.
FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization
We propose to extract medical facts of the input medical report, its gold summary, and candidate summaries based on the RadGraph schema and design the fact-guided reranker to efficiently incorporate the extracted medical facts for selecting the optimal summary.
I3D: Transformer architectures with input-dependent dynamic depth for speech recognition
Transformer-based end-to-end speech recognition has achieved great success.
Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and Understanding
Self-supervised speech representation learning (SSL) has shown to be effective in various downstream tasks, but SSL models are usually large and slow.
Improving Massively Multilingual ASR With Auxiliary CTC Objectives
In this paper, we introduce our work on improving performance on FLEURS, a 102-language open ASR benchmark, by conditioning the entire model on language identity (LID).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations
In the United States, primary open-angle glaucoma (POAG) is the leading cause of blindness, especially among African American and Hispanic individuals.
SpeechLMScore: Evaluating speech generation using speech language model
While human evaluation is the most reliable metric for evaluating speech generation systems, it is generally costly and time-consuming.
SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies
Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i. e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i. e., opioid use), and evaluate the extraction rate of SDoH using cancer populations.
Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances
However, DNNs are notoriously fragile to the class imbalance in image classification.
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding
Collecting sufficient labeled data for spoken language understanding (SLU) is expensive and time-consuming.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging
Our extensive experiments on popular medical imaging classification tasks (cardiopulmonary disease and lesion classification) using real-world datasets, show the performance benefit of RoS-KD, its ability to distill knowledge from many popular large networks (ResNet-50, DenseNet-121, MobileNet-V2) in a comparatively small network, and its robustness to adversarial attacks (PGD, FSGM).
E-Branchformer: Branchformer with Enhanced merging for speech recognition
Conformer, combining convolution and self-attention sequentially to capture both local and global information, has shown remarkable performance and is currently regarded as the state-of-the-art for automatic speech recognition (ASR).
Ranked #9 on
Speech Recognition
on LibriSpeech test-other
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study
Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings.
Ranked #1 on
Long-tail Learning
on MIMIC-CXR-LT
Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays
Using the learned self-attention of its image branch, RGT extracts a bounding box for which to compute radiomic features, which are further processed by the radiomics branch; learned image and radiomic features are then fused and mutually interact via cross-attention layers.
Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding
Conformer has proven to be effective in many speech processing tasks.
Classifying Cyber-Risky Clinical Notes by Employing Natural Language Processing
Clinical notes, which can be embedded into electronic medical records, document patient care delivery and summarize interactions between healthcare providers and patients.
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.
Prior Knowledge Enhances Radiology Report Generation
In this work, we propose to mine and represent the associations among medical findings in an informative knowledge graph and incorporate this prior knowledge with radiology report generation to help improve the quality of generated reports.
ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet
However, there are few open source toolkits that can be used to generate reproducible results on different Spoken Language Understanding (SLU) benchmarks.
CU-UD: text-mining drug and chemical-protein interactions with ensembles of BERT-based models
Our system obtained 0. 7708 in precision and 0. 7770 in recall, for an F1 score of 0. 7739, demonstrating the effectiveness of using ensembles of BERT-based language models for automatically detecting relations between chemicals and proteins.
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.
RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification
Radiology reports are unstructured and contain the imaging findings and corresponding diagnoses transcribed by radiologists which include clinical facts and negated and/or uncertain statements.
SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata
In this paper, we proposed a novel and simple data augmentation method based on patient metadata and supervised knowledge to create clinically accurate positive and negative augmentations for chest X-rays.
CheXT: Knowledge-Guided Cross-Attention Transformer for Abnormality Classification and Localization in Chest X-rays
During training, the image branch leverages its learned attention to estimate pathology localization, which is then utilized to extract radiomic features from images in the radiomics branch.
Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment
Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision.
A framework for massive scale personalized promotion
In order to do effective optimization in the second stage, counterfactual prediction and noise-reduction are essential for the first stage.
Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality
Utilizing clinical texts in survival analysis is difficult because they are largely unstructured.
Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction
In this work, we explore the method of employing contrastive learning to improve the text representation from the BERT model for relation extraction.
Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop
The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as a knowledge augmentation.
Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning
Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era.
Deep-Learned Broadband Encoding Stochastic Filters for Computational Spectroscopic Instruments
Computational spectroscopic instruments with Broadband Encoding Stochastic (BEST) filters allow the reconstruction of the spectrum at high precision with only a few filters.
Instrumentation and Detectors
Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays
After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions.
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.
Efficient Long-Range Convolutions for Point Clouds
We showcase this framework by introducing a neural network architecture that combines LRC-layers with short-range convolutional layers to accurately learn the energy and force associated with a $N$-body potential.
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.
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
Deep Optics for Single-shot High-dynamic-range Imaging
High-dynamic-range (HDR) imaging is crucial for many computer graphics and vision applications.
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.
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)
Depth and Transient Imaging With Compressive SPAD Array Cameras
Time-of-flight depth imaging and transient imaging are two imaging modalities that have recently received a lot of interest.
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.
Comment Generation for Source Code: State of the Art, Challenges and Opportunities
One way to make software development more efficient is to make the program more readable.
Software Engineering
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.
Revisiting Cross-Channel Information Transfer for Chromatic Aberration Correction
Image aberrations can cause severe degradation in image quality for consumer-level cameras, especially under the current tendency to reduce the complexity of lens designs in order to shrink the overall size of modules.
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.
Studying Relationships between Human Gaze, Description, and Computer Vision
We posit that user behavior during natural viewing of images contains an abundance of information about the content of images as well as information related to user intent and user defined content importance.
