Search Results for author:
Found 35 papers, 21 papers with code
Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues
Through quantitative evaluation of the linguistic quality, we observe that the dialog generation model - DialoGPT (345M) with transfer learning on video data attains scores similar to a human response baseline.
Medical Image Registration and Its Application in Retinal Images: A Review
Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities.
Apollo: An Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People
Despite the vast repository of global medical knowledge predominantly being in English, local languages are crucial for delivering tailored healthcare services, particularly in areas with limited medical resources.
ICON: Improving Inter-Report Consistency of Radiology Report Generation via Lesion-aware Mix-up Augmentation
Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated reports.
Me LLaMA: Foundation Large Language Models for Medical Applications
In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets.
Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET)
In this paper, we propose InterlaCed Encoder NETworks (i. e., ICE-NET) for antonym vs synonym distinction, that aim to capture and model the relation-specific properties of the antonyms and synonyms pairs in order to perform the classification task in a performance-enhanced manner.
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.
VSR-Net: Vessel-like Structure Rehabilitation Network with Graph Clustering
Based on this, we propose a novel Vessel-like Structure Rehabilitation Network (VSR-Net) to rehabilitate subsection ruptures and improve the model calibration based on coarse vessel-like structure segmentation results.
Metasurface Sensing Approach to DOA Estimation of Coherent Signals
After periodical coding, the DOA information of incident signals in the time domain is represented as the amplitude and phase information at different frequency points in the frequency domain.
Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancement
Additionally, a pseudo-label picker is developed to boost the knowledge distillation of enhancement tasks.
GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings
Bilingual Lexical Induction (BLI) is a core challenge in NLP, it relies on the relative isomorphism of individual embedding spaces.
GRI: Graph-based Relative Isomorphism of Word Embedding Spaces
Automated construction of bilingual dictionaries using monolingual embedding spaces is a core challenge in machine translation.
ACT-Net: Anchor-context Action Detection in Surgery Videos
In this paper, to accurately detect fine-grained actions that happen at every moment, we propose an anchor-context action detection network (ACTNet), including an anchor-context detection (ACD) module and a class conditional diffusion (CCD) module, to answer the following questions: 1) where the actions happen; 2) what actions are; 3) how confidence predictions are.
A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning
Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems.
Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation
Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains.
Identifying and Extracting Rare Disease Phenotypes with Large Language Models
Despite this, ChatGPT achieved similar or higher accuracy for certain entities (i. e., rare diseases and signs) in the one-shot setting (F1 of 0. 776 and 0. 725).
Label-noise-tolerant medical image classification via self-attention and self-supervised learning
Therefore, in this work, we innovatively devise noise-robust training approach to mitigate the adverse effects of noisy labels in medical image classification.
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.
Learnable Ophthalmology SAM
Based on Segment Anything (SAM), we propose a simple but effective learnable prompt layer suitable for multiple target segmentation in ophthalmology multi-modal images, named Learnable Ophthalmology Segment Anything (SAM).
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.
PPCR: Learning Pyramid Pixel Context Recalibration Module for Medical Image Classification
Spatial attention mechanism has been widely incorporated into deep convolutional neural networks (CNNs) via long-range dependency capturing, significantly lifting the performance in computer vision, but it may perform poorly in medical imaging.
Concealed Object Detection for Passive Millimeter-Wave Security Imaging Based on Task-Aligned Detection Transformer
Passive millimeter-wave (PMMW) is a significant potential technique for human security screening.
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module
Specifically, we propose a fusion module named Multi-scale Attention Fusion (MAF) module for our dual-stream framework to effectively integrate features of the two tasks.
Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network
For boosting the clinical deployment of fundus image enhancement, this paper proposes the pyramid constraint to develop a degradation-invariant enhancement network (PCE-Net), which mitigates the demand for clinical data and stably enhances unknown data.
SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image
Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases.
Structure-consistent Restoration Network for Cataract Fundus Image Enhancement
In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure.
An Annotation-free Restoration Network for Cataractous Fundus Images
The restoration model is learned from the synthesized images and adapted to real cataract images.
3D Vessel Reconstruction in OCT-Angiography via Depth Map Estimation
Experimental results demonstrate that our method is effective in the depth prediction and 3D vessel reconstruction for OCTA images.% results may be used to guide subsequent vascular analysis
Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey
This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images.
Attention-based Saliency Hashing for Ophthalmic Image Retrieval
The different grades or classes of ophthalmic images may be share similar overall performance but have subtle differences that can be differentiated by mining salient regions.
Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference
Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space.
Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT
We consider it to be the first work to detect angle-closure glaucoma by means of 3D representation.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Approximating the Backbone in the Weighted Maximum Satisfiability Problem
And then we presented a backbone guided local search (BGLS) with Walksat operator for weighted MAX-SAT.
A Hybrid ACO Algorithm for the Next Release Problem
In this paper, we propose a Hybrid Ant Colony Optimization algorithm (HACO) for Next Release Problem (NRP).
