HA-DSB uses a diffusion Schrödinger bridge with vision-language model region embeddings and PET-guided noise modulation to translate whole-body MRI while preserving lesion fidelity.
hub
ACM Trans
15 Pith papers cite this work, alongside 2,012 external citations. Polarity classification is still indexing.
15
Pith papers citing it
2,012
external citations · OpenAlex
hub tools
citation-role summary
method 1
citation-polarity summary
years
2026 15roles
method 1polarities
use method 1representative citing papers
General-domain OS-sLLMs moderately correlate with human OPTION12 SDM scores on Dutch melanoma transcripts; medical models fail via hallucination, and a Judge-LLM consensus is proposed.
Under constant-token and compute-matched budgets, continual pre-training on SE text slightly improves domain scores while preserving general language ability, whereas pre-training from scratch incurs large decisive losses except for small models with token-rich budgets.
Hybrid neural-symbolic pipeline extracts (action, date) pairs from clinical notes at 0.99 Pair F1 by using BioBERT tagging plus deterministic time normalization, outperforming LLMs on a synthetic benchmark with OOV actions.
DPR-BAG generates biomedical abstracts from full texts via BOMRC decomposition, parallel LLM summarization, and refinement, showing higher abstractive novelty than baselines while preserving factual consistency on a 46k-article PMC dataset.
Mean pooling and multi-window RGB encoding optimize vision-language performance on CT enterography, with retrieval-augmented generation substantially improving automated report severity accuracy over fine-tuning alone.
RAG-GNN augments GNNs with retrieved literature knowledge via gated fusion to improve functional clustering of 379 proteins in cancer signaling networks, raising silhouette score by 0.093.
HyEm maps radius-controlled hyperbolic ontology embeddings to Euclidean space for ANN indexing and applies query-adaptive hyperbolic reranking to improve hierarchy-aware retrieval while preserving most Euclidean performance on flat queries.
The authors created and released AAbAAC, an annotated corpus of 115 abstracts for autoimmunity information extraction, and showed NER performance gains after fine-tuning models on it.
PromptRad reformulates multi-label radiology report classification as masked language modeling and enriches verbalizers with UMLS synonyms, outperforming baselines with only 32 training examples.
Proposes a multi-modal multi-span medical QA framework and new dataset that outputs answers containing both text and relevant images.
Fine-tuned e5_large LLM reaches 0.866 F1_micro on ICD classification of 145k Spanish psychiatric texts, outperforming BoW, TF-IDF, and other transformers.
Fine-tuned LLaMA3 with LoRA reaches 81.24% F1 on 18-category fine-grained medical entity recognition, beating zero-shot by 63.11% and few-shot by 35.63%.
INTERACT is an AI-driven XR framework providing real-time sign language interpretation and emotion recognition for accessible virtual communication, with pilot tests showing 92% user satisfaction and high accuracy rates.
A hybrid RAG system with retrieval, Cohere reranking, and claim-level LLM judgment achieves 100% grounding accuracy on 200 claims from 25 biomedical queries in a pilot study.
citing papers explorer
-
Heterogeneity-Adaptive Diffusion Schrodinger Bridge for PET-Guided Whole-Body MRI Translation
HA-DSB uses a diffusion Schrödinger bridge with vision-language model region embeddings and PET-guided noise modulation to translate whole-body MRI while preserving lesion fidelity.
-
Measuring the practice of shared-decision making (OPTION12): An Investigation into Open-sourced Smaller LLMs (OS-sLLMs) for Better Privacy and Sustainability
General-domain OS-sLLMs moderately correlate with human OPTION12 SDM scores on Dutch melanoma transcripts; medical models fail via hallucination, and a Judge-LLM consensus is proposed.
-
Pre-Training on Software Engineering Texts: Effects on Domain Adaptation and General-Language Understanding
Under constant-token and compute-matched budgets, continual pre-training on SE text slightly improves domain scores while preserving general language ability, whereas pre-training from scratch incurs large decisive losses except for small models with token-rich budgets.
-
Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline
Hybrid neural-symbolic pipeline extracts (action, date) pairs from clinical notes at 0.99 Pair F1 by using BioBERT tagging plus deterministic time normalization, outperforming LLMs on a synthetic benchmark with OOV actions.
-
Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation
DPR-BAG generates biomedical abstracts from full texts via BOMRC decomposition, parallel LLM summarization, and refinement, showing higher abstractive novelty than baselines while preserving factual consistency on a 46k-article PMC dataset.
-
Representation geometry shapes task performance in vision-language modeling for CT enterography
Mean pooling and multi-window RGB encoding optimize vision-language performance on CT enterography, with retrieval-augmented generation substantially improving automated report severity accuracy over fine-tuning alone.
-
RAG-GNN: Integrating Retrieved Knowledge with Graph Neural Networks for Precision Medicine
RAG-GNN augments GNNs with retrieved literature knowledge via gated fusion to improve functional clustering of 379 proteins in cancer signaling networks, raising silhouette score by 0.093.
-
HyEm: Query-Adaptive Hyperbolic Retrieval for Biomedical Ontologies via Euclidean Vector Indexing
HyEm maps radius-controlled hyperbolic ontology embeddings to Euclidean space for ANN indexing and applies query-adaptive hyperbolic reranking to improve hierarchy-aware retrieval while preserving most Euclidean performance on flat queries.
-
AAbAAC: An Annotated Corpus for Autoimmunity Information Extraction
The authors created and released AAbAAC, an annotated corpus of 115 abstracts for autoimmunity information extraction, and showed NER performance gains after fine-tuning models on it.
-
PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling
PromptRad reformulates multi-label radiology report classification as masked language modeling and enriches verbalizers with UMLS synonyms, outperforming baselines with only 32 training examples.
-
$M^3 QuestionIng$: Multi-modal Multi-span Medical Question Answering
Proposes a multi-modal multi-span medical QA framework and new dataset that outputs answers containing both text and relevant images.
-
Automated ICD Classification of Psychiatric Diagnoses: From Classical NLP to Large Language Models
Fine-tuned e5_large LLM reaches 0.866 F1_micro on ICD classification of 145k Spanish psychiatric texts, outperforming BoW, TF-IDF, and other transformers.
-
Beyond the Basics: Leveraging Large Language Model for Fine-Grained Medical Entity Recognition
Fine-tuned LLaMA3 with LoRA reaches 81.24% F1 on 18-category fine-grained medical entity recognition, beating zero-shot by 63.11% and few-shot by 35.63%.
-
INTERACT: An AI-Driven Extended Reality Framework for Accesible Communication Featuring Real-Time Sign Language Interpretation and Emotion Recognition
INTERACT is an AI-driven XR framework providing real-time sign language interpretation and emotion recognition for accessible virtual communication, with pilot tests showing 92% user satisfaction and high accuracy rates.
-
A Hybrid Retrieval and Reranking Framework for Evidence-Grounded Retrieval-Augmented Generation
A hybrid RAG system with retrieval, Cohere reranking, and claim-level LLM judgment achieves 100% grounding accuracy on 200 claims from 25 biomedical queries in a pilot study.