SLAKE

The 'SLAKE' dataset/benchmark is used to evaluate the performance of models in Medical Visual Question Answering (MedVQA) by providing a collection of complex medical images and corresponding questions.

Papers using SLAKE (10)

Benchmarking GPT-5 For Zero-shot Multimodal Medical Reasoning In Radiology And Radiation Oncology2025 · 3 citesDual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA2026InViC: Intent-aware Visual Cues for Medical Visual Question Answering2026Robust Pre-Training of Medical Vision-and-Language Models with Domain-Invariant Multi-Modal Masked Reconstruction2026CMI-MTL: Cross-Mamba interaction based multi-task learning for medical visual question answering2025Llada-medv: Exploring Large Language Diffusion Models For Biomedical Image Understanding2025How Far Have Medical Vision-language Models Come? A Comprehensive Benchmarking Study2025Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding2025MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale2024 · 2 citesLaPA: Latent Prompt Assist Model For Medical Visual Question Answering2024 · 1 cites