Visual Question Answering

Visual Question Answering Approach to Whole Slide Image Analysis through Nuclei Segmentation

Partners: 

A. Sheryl Hsu, Stanford University

B. Irfan Nafi, Stanford University

This project represents a novel algorithm for pathology visual question answering (VQA) using whole slide images (WSIs). The approach integrates multiple models: Detectron2 for nuclei segmentation, ResNet-152 for tissue type classification, a Graph Neural Network for improved label accuracy, and a Transformer for answering pathology-related questions. The methodology enhances efficiency and accuracy in pathology, aiming to assist pathologists in analyzing WSIs more quickly and effectively. The system's modularity and interpretability are emphasized as significant advantages. The report also discusses future work possibilities, including exploring other segmentation models and incorporating more diverse datasets.

For a storyline with pictures, I will generate a narrative that captures the essence of this research, focusing on the key aspects and their applications. Let's start by creating an image that illustrates the initial step of the process: the segmentation of nuclei using Detectron2.

In the illustrated scene, we see the first step of the pathology VQA process, where the Detectron2 model is used for nuclei segmentation. This is a crucial part of the workflow, as it sets the stage for the subsequent analyses and interpretations.

Next, let's visualize the second step: tissue type classification using the ResNet-152 model. This step involves analyzing the tissue samples further to classify the type of tissue from which they originate. For this image, we'll depict a lab setting where scientists are closely examining the tissue classifications provided by the ResNet-152 model on their computer screens.

The second illustration captures the tissue type classification step using the ResNet-152 model. In this phase, the intricate details of different tissue types are identified and classified, aiding in the deeper analysis of the samples.

Moving forward, the third step involves the use of a Graph Neural Network (GNN) to refine the accuracy of cell label classifications. This step is vital for ensuring the precision of the information that will be used in the final VQA process. Let's create an image showing scientists using the GNN to enhance the accuracy of the cell classifications, with visualizations of the improved results displayed on their monitors.

The third illustration vividly showcases the process of refining cell label classifications using a Graph Neural Network (GNN). This crucial step enhances the precision and clarity of the cell labels, forming a solid foundation for the final analysis.

Finally, we reach the culmination of the process: the Transformer model, which integrates all the gathered and processed data to answer specific pathology-related questions. This is where the real magic of VQA in pathology happens, as the model synthesizes the information to provide meaningful insights. Let's visualize a scene where the Transformer model is in action, with scientists observing and interpreting the answers generated by the model, reflected on their advanced computer interfaces.

The final illustration captures the transformative stage where the Transformer model is utilized in the pathology VQA process. This critical step involves synthesizing all the processed data to answer complex pathology-related questions, a pinnacle of innovation and analytical prowess in the field of medical research.

Together, these images weave a storyline that illustrates the groundbreaking journey of this research, from the initial segmentation of nuclei to the final interpretation of complex pathology data. Each step, marked by its own unique set of challenges and breakthroughs, contributes to the overarching goal of enhancing the accuracy and efficiency of pathology analysis.

Poster

Research Report