DALPHIN benchmark finds the pathology-specific AI copilot PathChat+ shows no statistically significant difference from expert pathologists in 4 of 6 tasks, with general models matching in 1-2 tasks, on a diverse open dataset released for ongoing evaluation.
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Pathvqa: 30000+ questions for medical visual question answering
18 Pith papers cite this work. Polarity classification is still indexing.
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PlantInquiryVQA shows multimodal LLMs describe plant symptoms but struggle with clinical reasoning and diagnosis, with structured Chain of Inquiry improving correctness and reducing hallucinations.
KIRA is a unified architecture for visual RAG that reports 0.97 retrieval precision, 1.0 grounding, and 0.707 domain correctness across medical, circuit, satellite, and histopathology domains via hierarchical chunking, dual-path retrieval, and evidence-conditioned generation.
A new multi-frame VQA benchmark on volumetric MRI demonstrates that bounding-box supervised fine-tuning improves spatial grounding in VLMs over zero-shot baselines.
Instruction-tuned vision-language model PaveGPT, trained on a large unified pavement dataset, achieves substantial gains over general models in comprehensive, standard-compliant pavement condition assessment.
VISTA uses prefix resampling and a vision-aware attention score to address data imbalance and language prior bias in self-improvement training of MLLMs, yielding up to 13.66% gains on reasoning tasks.
Self-verification in medical VQA creates a verification mirage where verifiers exhibit high error and agreement bias on wrong answers, with reliability strongly conditioned on task type.
RadThinking releases a large longitudinal CT VQA dataset stratified into foundation perception questions, single-rule reasoning questions, and compositional multi-step chains grounded in clinical reporting standards for cancer screening.
MedSynapse-V evolves latent diagnostic memories via meta queries, causal counterfactual refinement with RL, and dual-branch memory transition to outperform prior medical VLM methods in diagnostic accuracy.
DCI unifies backdoor adjustment and instrumental variable learning in MedVQA to extract deconfounded representations, yielding better out-of-distribution performance on SLAKE, VQA-RAD and similar benchmarks.
Dialectic-Med uses proponent-opponent-mediator agents with visual falsification to enforce grounded diagnostic reasoning in MLLMs, achieving SOTA accuracy and reduced hallucinations on MIMIC-CXR-VQA, VQA-RAD, and PathVQA.
A trajectory-aware process reward using DTW on sentence embeddings, combined with exact-match in GRPO after SFT, raises mean medical VQA accuracy from 0.598 to 0.689 across six benchmarks.
InternVL 2.5 is the first open-source MLLM to surpass 70% on the MMMU benchmark via model, data, and test-time scaling, with a 3.7-point gain from chain-of-thought reasoning.
LiteMedCoT-VL distills chain-of-thought from a 235B model to 2B VLMs via LoRA, reaching 64.9% accuracy on PMC-VQA and beating a 4B zero-shot baseline by 11 points.
A Medical Entity Tree organizes medical knowledge to engineer higher-quality training data that boosts general MLLMs on medical benchmarks.
BiasCareVL is a bias-aware vision-language framework trained on 3.44 million medical samples that outperforms prior methods on clinical tasks like diagnosis and segmentation while aiming for equitable performance under data imbalances.
MAny addresses dual-forgetting in multimodal continual instruction tuning via CPM and LPM merging strategies, delivering up to 8.57% accuracy gains on UCIT benchmarks without additional training.
citing papers explorer
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DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset
DALPHIN benchmark finds the pathology-specific AI copilot PathChat+ shows no statistically significant difference from expert pathologists in 4 of 6 tasks, with general models matching in 1-2 tasks, on a diverse open dataset released for ongoing evaluation.
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Thinking Like a Botanist: Challenging Multimodal Language Models with Intent-Driven Chain-of-Inquiry
PlantInquiryVQA shows multimodal LLMs describe plant symptoms but struggle with clinical reasoning and diagnosis, with structured Chain of Inquiry improving correctness and reducing hallucinations.
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KIRA: Knowledge-Intensive Image Retrieval and Reasoning Architecture for Specialized Visual Domains
KIRA is a unified architecture for visual RAG that reports 0.97 retrieval precision, 1.0 grounding, and 0.707 domain correctness across medical, circuit, satellite, and histopathology domains via hierarchical chunking, dual-path retrieval, and evidence-conditioned generation.
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Beyond a Single Frame: Multi-Frame Spatially Grounded Reasoning Across Volumetric MRI
A new multi-frame VQA benchmark on volumetric MRI demonstrates that bounding-box supervised fine-tuning improves spatial grounding in VLMs over zero-shot baselines.
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Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment
Instruction-tuned vision-language model PaveGPT, trained on a large unified pavement dataset, achieves substantial gains over general models in comprehensive, standard-compliant pavement condition assessment.
-
Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training
VISTA uses prefix resampling and a vision-aware attention score to address data imbalance and language prior bias in self-improvement training of MLLMs, yielding up to 13.66% gains on reasoning tasks.
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Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA
Self-verification in medical VQA creates a verification mirage where verifiers exhibit high error and agreement bias on wrong answers, with reliability strongly conditioned on task type.
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RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology
RadThinking releases a large longitudinal CT VQA dataset stratified into foundation perception questions, single-rule reasoning questions, and compositional multi-step chains grounded in clinical reporting standards for cancer screening.
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MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V evolves latent diagnostic memories via meta queries, causal counterfactual refinement with RL, and dual-branch memory transition to outperform prior medical VLM methods in diagnostic accuracy.
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Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA
DCI unifies backdoor adjustment and instrumental variable learning in MedVQA to extract deconfounded representations, yielding better out-of-distribution performance on SLAKE, VQA-RAD and similar benchmarks.
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Dialectic-Med: Mitigating Diagnostic Hallucinations via Counterfactual Adversarial Multi-Agent Debate
Dialectic-Med uses proponent-opponent-mediator agents with visual falsification to enforce grounded diagnostic reasoning in MLLMs, achieving SOTA accuracy and reduced hallucinations on MIMIC-CXR-VQA, VQA-RAD, and PathVQA.
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Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
InternVL 2.5 is the first open-source MLLM to surpass 70% on the MMMU benchmark via model, data, and test-time scaling, with a 3.7-point gain from chain-of-thought reasoning.
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LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering
LiteMedCoT-VL distills chain-of-thought from a 235B model to 2B VLMs via LoRA, reaching 64.9% accuracy on PMC-VQA and beating a 4B zero-shot baseline by 11 points.
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Learning from Medical Entity Trees: An Entity-Centric Medical Data Engineering Framework for MLLMs
A Medical Entity Tree organizes medical knowledge to engineer higher-quality training data that boosts general MLLMs on medical benchmarks.
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Bias-constrained multimodal intelligence for equitable and reliable clinical AI
BiasCareVL is a bias-aware vision-language framework trained on 3.44 million medical samples that outperforms prior methods on clinical tasks like diagnosis and segmentation while aiming for equitable performance under data imbalances.
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MAny: Merge Anything for Multimodal Continual Instruction Tuning
MAny addresses dual-forgetting in multimodal continual instruction tuning via CPM and LPM merging strategies, delivering up to 8.57% accuracy gains on UCIT benchmarks without additional training.