REVIEW 7 cited by
SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Medical visual question answering (Med-VQA) has tremendous potential in healthcare. However, the development of this technology is hindered by the lacking of publicly-available and high-quality labeled datasets for training and evaluation. In this paper, we present a large bilingual dataset, SLAKE, with comprehensive semantic labels annotated by experienced physicians and a new structural medical knowledge base for Med-VQA. Besides, SLAKE includes richer modalities and covers more human body parts than the currently available dataset. We show that SLAKE can be used to facilitate the development and evaluation of Med-VQA systems. The dataset can be downloaded from http://www.med-vqa.com/slake.
Forward citations
Cited by 7 Pith papers
-
MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation
On a new benchmark of 5,620 real multimodal online consultations, top LLMs trail the original physicians mainly because they trigger more unsafe or unsupported negative criteria.
-
RadFusion: Towards Threshold-Controllable Radiology Report Generation
RadFusion makes radiology reports threshold-controllable by having an LLM rewrite classifier-guided findings, yet the reported ROC conformance follows by construction.
-
Ming-Omni: A Unified Multimodal Model for Perception and Generation
A single model with modality-specific routing processes image, text, audio, and video inputs and generates text, speech, and images, with public benchmarks reported across all of these abilities.
-
The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models
Continued pretraining of open LLMs and VLMs on biomedical data yields little or no consistent improvement over their base models on closed-ended medical QA in zero-/few-shot and supervised fine-tuning regimes.
-
RARL: Improving Medical VLM Reasoning and Generalization with Reinforcement Learning and LoRA under Data and Hardware Constraints
RARL fine-tunes Qwen2-VL-2B on 716 medical samples with GRPO, LoRA, and a vaguely defined reasoning reward, claiming gains of 7.78% over SFT on reasoning and up to 27% on unseen VQA benchmarks.
-
Inference-Time Agentic Decision Rules Beat Longer Evolving Search for Multi-Image Medical Reasoning
On MedFrameQA, order-vote (57.89%) beats fixed prompting (52.73%) and order-rerank (55.79%), and a single 100-generation run drops final-test accuracy to 56.02%.
-
Adapting Lightweight Vision Language Models for Radiological Visual Question Answering
A 3B PaliGemma model fine-tuned with synthetic QA pairs and two-stage training reaches 41.5% accuracy on open-ended radiology VQA, about 15 points below LLaVA-Med.
Discussion (0). Continue with ORCID to comment.