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Found 31 papers, 16 papers with code
Give me Some Hard Questions: Synthetic Data Generation for Clinical QA
Clinical Question Answering (QA) systems enable doctors to quickly access patient information from electronic health records (EHRs).
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
Additionally, we propose M3D-LaMed, a versatile multi-modal large language model for 3D medical image analysis.
Prediction of vaccination coverage level in the heterogeneous mixing population
Heterogeneity of population is a key factor in modeling the transmission of disease among the population and has huge impact on the outcome of the transmission.
UAE: Universal Anatomical Embedding on Multi-modality Medical Images
They use self-supervised learning to acquire a discriminative embedding for each voxel within the image.
SegVol: Universal and Interactive Volumetric Medical Image Segmentation
Precise image segmentation provides clinical study with instructive information.
PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep understanding of both instincts of large language models (LLMs) and the intricacies of the target task.
A Consumer-tier based Visual-Brain Machine Interface for Augmented Reality Glasses Interactions
Here we develop a consumer-tier Visual-Brain Machine Inteface(V-BMI) system specialized for Augmented Reality(AR) glasses interactions.
SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation
We evaluate our method on liver tumor segmentation and achieve state-of-the-art performance, outperforming traditional fine-tuning with only 6% of tunable parameters, also achieving 94% of full-data performance by labeling only 5% of the data.
Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images
Specifically, to liberate labor, we design a novel discrepancy decoder model and a CAMPUS (CAM, Pseudo-label and groUnd-truth Selection) criterion to replace the noisy CAMs with accurate model predictions and a few human labels.
Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Images
We then use this SAM to identify corresponding regions on paired images using robust grid-points matching, followed by a point-set based affine/rigid registration, and a deformable fine-tuning step to produce registered paired images.
Schema-Driven Information Extraction from Heterogeneous Tables
We use this collection of annotated tables to evaluate the ability of open-source and API-based language models to extract information from tables covering diverse domains and data formats.
Ranked #1 on
Attribute Extraction
on SWDE
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding
Cross-task knowledge transfer via multi-task learning has recently made remarkable progress in general NLP tasks.
Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts
As far as we are aware, this is the first work to present experimental results demonstrating the effectiveness of in-context learning on the task of few-shot anaphora resolution in scientific protocols.
SynKB: Semantic Search for Synthetic Procedures
In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols.
C3-STISR: Scene Text Image Super-resolution with Triple Clues
In this paper, we present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution.
An age-of-infection model with both symptomatic and asymptomatic infections
We formulate a general age-of-infection epidemic model with two pathways: the symptomatic infections and the asymptomatic infections.
Hierarchical Policy for Non-prehensile Multi-object Rearrangement with Deep Reinforcement Learning and Monte Carlo Tree Search
Non-prehensile multi-object rearrangement is a robotic task of planning feasible paths and transferring multiple objects to their predefined target poses without grasping.
Pre-train or Annotate? Domain Adaptation with a Constrained Budget
Our experiments suggest task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain.
Membraneless organelles formed by liquid-liquid phase separation increase bacterial fitness
Liquid-liquid phase separation is emerging as a crucial phenomenon in several fundamental cell processes.
Federated Face Recognition
Face recognition has been extensively studied in computer vision and artificial intelligence communities in recent years.
Process-Level Representation of Scientific Protocols with Interactive Annotation
We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, long-range coreference, grounding, and implicit arguments.
IPM Move Planner: AN EFFICIENT EXPLOITING DEEP REINFORCEMENT LEARNING WITH MONTE CARLO TREE SEARCH
The literature about scene rearrangement focus on developing a move planner totransform a pair of layouts on a limited plane.
Text Recognition in Real Scenarios with a Few Labeled Samples
Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications.
Structured Minimally Supervised Learning for Neural Relation Extraction
Our approach achieves state-of-the-art results on minimally supervised sentential relation extraction, outperforming a number of baselines, including a competitive approach that uses the attention layer of a purely neural model.
Spatial clustering and common regulatory elements correlate with coordinated gene expression
Many cellular responses to surrounding cues require temporally concerted transcriptional regulation of multiple genes.
Edit Probability for Scene Text Recognition
The advantage lies in that the training process can focus on the missing, superfluous and unrecognized characters, and thus the impact of the misalignment problem can be alleviated or even overcome.
AON: Towards Arbitrarily-Oriented Text Recognition
Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts.
Ranked #9 on
Scene Text Recognition
on ICDAR 2003
Focusing Attention: Towards Accurate Text Recognition in Natural Images
FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images.
Label Propagation on K-partite Graphs with Heterophily
In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption.
Online Learning for Wireless Distributed Computing
To the best of our knowledge, MABSTA is the first online algorithm in this domain of task assignment problems and provides provable performance guarantee.
