bibtext: @InProceedings{ Rad_ThyGraph_MICCAI2024,
                   author = { Radhachandran, Ashwath and Vittalam, Alekhya and Ivezic, Vedrana and Sant, Vivek and Athreya, Shreeram and Moleta, Chace and Patel, Maitraya and Masamed, Rinat and Arnold, Corey and Speier, William },
                   title = { { ThyGraph: A Graph-Based Approach for Thyroid Nodule Diagnosis from Ultrasound Studies } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: Improved thyroid nodule risk stratification from ultrasound (US) can mitigate overdiagnosis and unnecessary biopsies. Previous studies often train deep learning models using manually selected single US frames; these approaches deviate from clinical practice where physicians utilize multiple image views for diagnosis. This paper introduces ThyGraph, a novel graph-based approach that improves feature aggregation and correlates anatomically proximate images, by leveraging spatial information to model US image studies as patient-level graphs. Graph convolutional networks are trained on image-based and patch-based graphs generated from 505 US image studies to predict nodule malignancy. Self-attention graph pooling is introduced to produce a node-level interpretability metric that is visualized downstream to identify important inputs. Our best performing model demonstrated an AUROC of 0.866±0.019 and AUPRC of 0.749±0.043 across five-fold cross validation, significantly outperforming two previously published attention-based feature aggregation networks. These previous studies fail to account for spatial dependencies by modeling images within a study as independent, uncorrelated instances. In the proposed graph paradigm, ThyGraph can effectively aggregate information across views of a nodule and take advantage of inter-image dependencies to improve nodule risk stratification, leading to better patient triaging and reducing reliance on biopsies. Code is available at https://github.com/ashwath-radha/ThyGraph.
title: ThyGraph: A Graph-Based Approach for Thyroid Nodule Diagnosis from Ultrasound Studies
authors: Radhachandran, Ashwath Vittalam, Alekhya Ivezic, Vedrana Sant, Vivek Athreya, Shreeram Moleta, Chace Patel, Maitraya Masamed, Rinat Arnold, Corey Speier, William