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Found 84 papers, 62 papers with code
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts
Chinese word segmentation (CWS) and medical concept recognition are two fundamental tasks to process Chinese electronic medical records (EMRs) and play important roles in downstream tasks for understanding Chinese EMRs.
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction
Few-shot Relation Extraction refers to fast adaptation to novel relation classes with few samples through training on the known relation classes.
A Label-Aware Autoregressive Framework for Cross-Domain NER
In doing so, label information contained in the embedding vectors can be effectively transferred to the target domain, and Bi-LSTM can further model the label relationship among different domains by pre-train and then fine-tune setting.
Cross-Domain Named Entity Recognition
named-entity-recognition
+2
Simplify RLHF as Reward-Weighted SFT: A Variational Method
More specifically, by directly minimizing the distribution gap between the learning LLM policy and the optimal solution of RLHF, we transform the alignment objective into a reward-driven re-weighted supervised fine-tuning (SFT) form, which only requires minor adjustment on the SFT loss to obtain noticeable improvement on training stability and effectiveness.
BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models
This dataset includes 818 pairs of high-resolution CXR and dual-energy subtraction soft tissue images collected from a partner hospital.
Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence
Notably, UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers and matches the performance of expert-level sonographers in the joint diagnosis of 8 common systemic diseases.
Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs
To address these challenges, we introduce a self-instructed in-context learning framework that empowers LLMs to deliver more effective responses by generating reliable derived prompts to construct informative contextual environments.
An Empirical Study on Information Extraction using Large Language Models
To demonstrate the latest representative progress in LLMs' information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types.
Atoxia: Red-teaming Large Language Models with Target Toxic Answers
Given a particular harmful answer, Atoxia generates a corresponding user query and a misleading answer opening to examine the internal defects of a given LLM.
Let Video Teaches You More: Video-to-Image Knowledge Distillation using DEtection TRansformer for Medical Video Lesion Detection
The teacher network aims at extracting temporal contexts from multiple frames and transferring them to the student network, and the student network is an image-based model dedicated to fast prediction in inference.
Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development
Based on this dataset, we further introduce a benchmark model for colorectal cancer segmentation, named the Adaptive Sparse-context TRansformer (ASTR).
Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models
Finally, source domain labels guide the diffusion model to generate image-label pairs for the target domain.
ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation
Furthermore, we carefully design a TNet module in our adaptation architecture to yield geometry constraints and obtain better depth quality.
CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
This study introduces Chain-of-Diagnosis (CoD) to enhance the interpretability of LLM-based medical diagnostics.
Pilot-Based SFO Estimation for Bistatic Integrated Sensing and Communication
Enabling bistatic radar sensing within the context of integrated sensing and communication (ISAC) for future sixth generation mobile networks demands strict synchronization accuracy, which is particularly challenging to be achieved with over-the-air synchronization.
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
The rapid development of multimodal large language models (MLLMs), such as GPT-4V, has led to significant advancements.
LLMs for Doctors: Leveraging Medical LLMs to Assist Doctors, Not Replace Them
Based on this, we construct a Chinese medical dataset called DoctorFLAN to support the entire workflow of doctors, which includes 92K Q\&A samples from 22 tasks and 27 specialists.
Intensity Confusion Matters: An Intensity-Distance Guided Loss for Bronchus Segmentation
We regard a voxel as a hard sample if it is in: (1) the background and has an intensity value close to the bronchus region; (2) the bronchus region and is of higher intensity than most voxels inside the bronchus; (3) the background region and at a short distance from the bronchus.
Self-Supervised Alignment Learning for Medical Image Segmentation
In this paper, we propose a novel self-supervised alignment learning framework to pre-train the neural network for medical image segmentation.
LLMs Could Autonomously Learn Without External Supervision
In the quest for super-human performance, Large Language Models (LLMs) have traditionally been tethered to human-annotated datasets and predefined training objectives-a process that is both labor-intensive and inherently limited.
UWAFA-GAN: Ultra-Wide-Angle Fluorescein Angiography Transformation via Multi-scale Generation and Registration Enhancement
Fundus photography, in combination with the ultra-wide-angle fundus (UWF) techniques, becomes an indispensable diagnostic tool in clinical settings by offering a more comprehensive view of the retina.
MileBench: Benchmarking MLLMs in Long Context
Despite the advancements and impressive performance of Multimodal Large Language Models (MLLMs) on benchmarks, their effectiveness in real-world, long-context, and multi-image tasks is unclear due to the benchmarks' limited scope.
Extracting Clean and Balanced Subset for Noisy Long-tailed Classification
To solve the joint issue of long-tailed distribution and label noise, most previous works usually aim to design a noise detector to distinguish the noisy and clean samples.
Apollo: A 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.
Large Multimodal Agents: A Survey
In this paper, we conduct a systematic review of LLM-driven multimodal agents, which we refer to as large multimodal agents ( LMAs for short).
Cell Graph Transformer for Nuclei Classification
Nuclei classification is a critical step in computer-aided diagnosis with histopathology images.
UniCell: Universal Cell Nucleus Classification via Prompt Learning
The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis.
ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
Large vision-language models (LVLMs) have shown premise in a broad range of vision-language tasks with their strong reasoning and generalization capabilities.
ICHPro: Intracerebral Hemorrhage Prognosis Classification Via Joint-attention Fusion-based 3d Cross-modal Network
Intracerebral Hemorrhage (ICH) is the deadliest subtype of stroke, necessitating timely and accurate prognostic evaluation to reduce mortality and disability.
nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model
Extensive experiments on 6 datasets demonstrate nnMamba's superiority over state-of-the-art methods in a suite of challenging tasks, including 3D image segmentation, classification, and landmark detection.
ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp Detection
Accurate polyp detection is critical for early colorectal cancer diagnosis.
BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images
As a remedial measure, bone suppression techniques have been introduced.
MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria
To this end, in our paper, we propose a new evaluation paradigm for MLLMs, which is evaluating MLLMs with per-sample criteria using potent MLLM as the judge.
HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs
We validate the new protocol in the domains where proprietary LLMs like ChatGPT perform relatively poorly, such as Traditional Chinese Medicine.
GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration
Intracerebral Hemorrhage (ICH) is a severe condition resulting from damaged brain blood vessel ruptures, often leading to complications and fatalities.
Diffusion-based Data Augmentation for Nuclei Image Segmentation
Therefore, augmenting a dataset with only a few labeled images to improve the segmentation performance is of significant research and application value.
Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images
Afterward, we train a separate segmentation model for each category using the images in the corresponding category.
Prompt-based Grouping Transformer for Nucleus Detection and Classification
Automatic nuclei detection and classification can produce effective information for disease diagnosis.
ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI Segmentation
To address the task, we propose a new UDA framework based on Appearance and Structure Consistency, named ASC.
Visual-Attribute Prompt Learning for Progressive Mild Cognitive Impairment Prediction
Furthermore, we propose a Prompt fine-Tuning (PT) scheme to transfer the knowledge from AD prediction task for progressive MCI (pMCI) diagnosis.
Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection
Multi-class cell nuclei detection is a fundamental prerequisite in the diagnosis of histopathology.
AceGPT, Localizing Large Language Models in Arabic
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models
Colonoscopy analysis, particularly automatic polyp segmentation and detection, is essential for assisting clinical diagnosis and treatment.
PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User Simulator
The unparalleled performance of closed-sourced ChatGPT has sparked efforts towards its democratization, with notable strides made by leveraging real user and ChatGPT dialogues, as evidenced by Vicuna.
CMB: A Comprehensive Medical Benchmark in Chinese
We hope this benchmark provide first-hand experience in existing LLMs for medicine and also facilitate the widespread adoption and enhancement of medical LLMs within China.
Advancing Visual Grounding with Scene Knowledge: Benchmark and Method
Therefore, in this paper, we propose a novel benchmark of \underline{S}cene \underline{K}nowledge-guided \underline{V}isual \underline{G}rounding (SK-VG), where the image content and referring expressions are not sufficient to ground the target objects, forcing the models to have a reasoning ability on the long-form scene knowledge.
Bridging Vision and Language Encoders: Parameter-Efficient Tuning for Referring Image Segmentation
Parameter Efficient Tuning (PET) has gained attention for reducing the number of parameters while maintaining performance and providing better hardware resource savings, but few studies investigate dense prediction tasks and interaction between modalities.
Ranked #4 on
Referring Expression Segmentation
on RefCOCO
On the Difference of BERT-style and CLIP-style Text Encoders
Masked language modeling (MLM) has been one of the most popular pretraining recipes in natural language processing, e. g., BERT, one of the representative models.
Injecting Knowledge into Biomedical Pre-trained Models via Polymorphism and Synonymous Substitution
This gives us a hint that relational knowledge might not be redundant to the stored knowledge of PLMs, but rather be complementary.
HuatuoGPT, towards Taming Language Model to Be a Doctor
Experimental results demonstrate that HuatuoGPT achieves state-of-the-art results in performing medical consultation among open-source LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets.
An Empirical Study on Information Extraction using Large Language Models
To demonstrate the latest representative progress in LLMs' information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types.
Huatuo-26M, a Large-scale Chinese Medical QA Dataset
Moreover, we also experimentally show the benefit of the proposed dataset in many aspects: (i) trained models for other QA datasets in a zero-shot fashion; and (ii) as external knowledge for retrieval-augmented generation (RAG); and (iii) improving existing pre-trained language models by using the QA pairs as a pre-training corpus in continued training manner.
Phoenix: Democratizing ChatGPT across Languages
This paper presents our efforts to democratize ChatGPT across language.
Structure Embedded Nucleus Classification for Histopathology Images
Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations.
Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts
Medical vision-and-language pre-training (Med-VLP) has shown promising improvements on many downstream medical tasks owing to its applicability to extracting generic representations from medical images and texts.
Lesion-aware Dynamic Kernel for Polyp Segmentation
Automatic and accurate polyp segmentation plays an essential role in early colorectal cancer diagnosis.
Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework
Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H\&E stained pathology images.
Document-level Relation Extraction with Relation Correlations
Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem.
Toward expanding the scope of radiology report summarization to multiple anatomies and modalities
We then conduct extensive experiments to evaluate the performance of models both within and across modality-anatomy pairs in MIMIC-RRS.
Improving Radiology Summarization with Radiograph and Anatomy Prompts
The impression is crucial for the referring physicians to grasp key information since it is concluded from the findings and reasoning of radiologists.
View-Disentangled Transformer for Brain Lesion Detection
Deep neural networks (DNNs) have been widely adopted in brain lesion detection and segmentation.
Attentive Symmetric Autoencoder for Brain MRI Segmentation
Self-supervised learning methods based on image patch reconstruction have witnessed great success in training auto-encoders, whose pre-trained weights can be transferred to fine-tune other downstream tasks of image understanding.
Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training
Besides, we conduct further analysis to better verify the effectiveness of different components of our approach and various settings of pre-training.
Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledge
Most existing methods mainly contain three elements: uni-modal encoders (i. e., a vision encoder and a language encoder), a multi-modal fusion module, and pretext tasks, with few studies considering the importance of medical domain expert knowledge and explicitly exploiting such knowledge to facilitate Med-VLP.
Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
Weakly Supervised Object Localization (WSOL), which aims to localize objects by only using image-level labels, has attracted much attention because of its low annotation cost in real applications.
Ranked #2 on
Weakly-Supervised Object Localization
on ImageNet
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods.
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction
We argue that relation information can be introduced more explicitly and effectively into the model.
Hero-Gang Neural Model For Named Entity Recognition
Named entity recognition (NER) is a fundamental and important task in NLP, aiming at identifying named entities (NEs) from free text.
Ranked #1 on
Named Entity Recognition (NER)
on WNUT 2016
Cross-modal Memory Networks for Radiology Report Generation
Medical imaging plays a significant role in clinical practice of medical diagnosis, where the text reports of the images are essential in understanding them and facilitating later treatments.
Graph Enhanced Contrastive Learning for Radiology Findings Summarization
To address the limitation, we propose a unified framework for exploiting both extra knowledge and the original findings in an integrated way so that the critical information (i. e., key words and their relations) can be extracted in an appropriate way to facilitate impression generation.
WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking
In this work, we propose WebUAV-3M, the largest public UAV tracking benchmark to date, to facilitate both the development and evaluation of deep UAV trackers.
Word Graph Guided Summarization for Radiology Findings
In this paper, we propose a novel method for automatic impression generation, where a word graph is constructed from the findings to record the critical words and their relations, then a Word Graph guided Summarization model (WGSum) is designed to generate impressions with the help of the word graph.
Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning
Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19.
Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks
Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities.
Ranked #12 on
Relation Extraction
on SemEval-2010 Task-8
Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference
In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset.
Field Embedding: A Unified Grain-Based Framework for Word Representation
Word representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings.
Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data Analysis
Non-negative matrix factorization (NMF) is a powerful tool for dimensionality reduction and clustering.
USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning
To alleviate this problem, an US dataset named US-4 is constructed for direct pretraining on the same domain.
Multi-Modal Active Learning for Automatic Liver Fibrosis Diagnosis based on Ultrasound Shear Wave Elastography
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD).
Generating Radiology Reports via Memory-driven Transformer
Particularly, this is the first work reporting the generation results on MIMIC-CXR to the best of our knowledge.
Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text.
Ranked #3 on
Named Entity Recognition (NER)
on WNUT 2016
Chinese Named Entity Recognition
named-entity-recognition
+2
Named Entity Recognition for Social Media Texts with Semantic Augmentation
In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively.
Ranked #4 on
Named Entity Recognition (NER)
on WNUT 2016
Chinese Named Entity Recognition
named-entity-recognition
+3
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification
Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic.
