# PitVQA: Image-grounded Text Embedding LLM for Visual Question Answering in Pituitary Surgery

arXiv: https://arxiv.org/abs/2405.13949 · HTML: https://mlsanity.com/paper/2405.13949/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 25 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [MUTAN: Multimodal Tucker Fusion for Visual Question Answering](https://mlsanity.com/paper/1705.06676/) (2017)
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://mlsanity.com/paper/1803.02579/) (2018)
- [Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering](https://mlsanity.com/paper/1708.01471/) (2017)
- [Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering](https://mlsanity.com/paper/1708.03619/) (2017)
- [Surgical-VQA: Visual Question Answering in Surgical Scenes using Transformer](https://mlsanity.com/paper/2206.11053/) (2022)
- [SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgery](https://mlsanity.com/paper/2304.09974/) (2023)
- [Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery](https://mlsanity.com/paper/2305.11692/) (2023)
- [Advancing Surgical VQA with Scene Graph Knowledge](https://mlsanity.com/paper/2312.10251/) (2023)
- [CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery](https://mlsanity.com/paper/2307.05182/) (2023)

## Built on by (most cited first, 23 of 23)

- [SurgVLM: A Large Vision-Language Model and Systematic Evaluation Benchmark for Surgical Intelligence](https://mlsanity.com/paper/2506.02555/) (2025)
- [VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding](https://mlsanity.com/paper/2412.02186/) (2024)
- [PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery](https://mlsanity.com/paper/2409.01184/) (2024)
- [From Screens to Scenes: A Survey of Embodied AI in Healthcare](https://mlsanity.com/paper/2501.07468/) (2025)
- [Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2506.18378/) (2025)
- [FunBench: Benchmarking Fundus Reading Skills of MLLMs](https://mlsanity.com/paper/2503.00901/) (2025)
- [AGIR: Assessing 3D Gait Impairment with Reasoning based on LLMs](https://mlsanity.com/paper/2503.18141/) (2025)
- [Specialized Foundation Models for Intelligent Operating Rooms](https://mlsanity.com/paper/2505.12890/) (2025)
- [When to Trust the Answer: Question-Aligned Semantic Nearest Neighbor Entropy for Safer Surgical VQA](https://mlsanity.com/paper/2511.01458/) (2025)
- [MedHorizon: Towards Long-context Medical Video Understanding in the Wild](https://mlsanity.com/paper/2605.06537/) (2026)
- [VIOLA: Towards Video In-Context Learning with Minimal Annotations](https://mlsanity.com/paper/2601.15549/) (2026)
- [Surg-R1: A Hierarchical Reasoning Foundation Model for Scalable and Interpretable Surgical Decision Support with Multi-Center Clinical Validation](https://mlsanity.com/paper/2603.12430/) (2026)
- [SurgSigma: A Spectrum of Large-Scale Multimodal Data and Foundation Models for Surgical Intelligence](https://mlsanity.com/paper/2603.16822/) (2026)
- [SurgCheck: Do Vision-Language Models Really Look at Images in Surgical VQA?](https://mlsanity.com/paper/2605.01911/) (2026)
- [SurgAtlas: A Large-Scale Surgical Video-Language Dataset with 2,391 Hours of Open and Minimally Invasive Surgery](https://mlsanity.com/paper/2606.25905/) (2026)
- [SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy](https://mlsanity.com/paper/2603.29962/) (2026)
- [OR-VSKC: Resolving Visual-Semantic Knowledge Conflicts in Operating Rooms with Synthetic Data-Guided Alignment](https://mlsanity.com/paper/2506.22500/) (2025)
- [SurgAnt-ViVQA: Learning to Anticipate Surgical Events through GRU-Driven Temporal Cross-Attention](https://mlsanity.com/paper/2511.03178/) (2025)
- [TemporalDoRA: Temporal PEFT for Robust Surgical Video Question Answering](https://mlsanity.com/paper/2603.09696/) (2026)
- [PanORama: Multiview Consistent Panoptic Segmentation in Operating Rooms](https://mlsanity.com/paper/2603.19920/) (2026)
- [A Comparative Study in Surgical AI: Potential and Limitations of Data, Compute, and Scaling](https://mlsanity.com/paper/2603.27341/) (2026)
- [MedStreamBench: A Time-Aware Benchmark for Streaming and Proactive Medical Video Understanding](https://mlsanity.com/paper/2607.01751/) (2026)
- [STSG-VQA: Evidence-Grounded Temporal Question Answering from Surgical Spatio-Temporal Scene Graphs](https://mlsanity.com/paper/2609.08543/) (2026)

## Abstract (arXiv)

Visual Question Answering (VQA) within the surgical domain, utilizing Large Language Models (LLMs), offers a distinct opportunity to improve intra-operative decision-making and facilitate intuitive surgeon-AI interaction. However, the development of LLMs for surgical VQA is hindered by the scarcity of diverse and extensive datasets with complex reasoning tasks. Moreover, contextual fusion of the image and text modalities remains an open research challenge due to the inherent differences between these two types of information and the complexity involved in aligning them. This paper introduces PitVQA, a novel dataset specifically designed for VQA in endonasal pituitary surgery and PitVQA-Net, an adaptation of the GPT2 with a novel image-grounded text embedding for surgical VQA. PitVQA comprises 25 procedural videos and a rich collection of question-answer pairs spanning crucial surgical aspects such as phase and step recognition, context understanding, tool detection and localization, and tool-tissue interactions. PitVQA-Net consists of a novel image-grounded text embedding that projects image and text features into a shared embedding space and GPT2 Backbone with an excitation block classification head to generate contextually relevant answers within the complex domain of endonasal pituitary surgery. Our image-grounded text embedding leverages joint embedding, cross-attention and contextual representation to understand the contextual relationship between questions and surgical images. We demonstrate the effectiveness of PitVQA-Net on both the PitVQA and the publicly available EndoVis18-VQA dataset, achieving improvements in balanced accuracy of 8% and 9% over the most recent baselines, respectively. Our code and dataset is available at https://github.com/mobarakol/PitVQA.