# Personalized Diagnostic Tool for Thyroid Cancer Classification using Multi-view Ultrasound

Source: [Personalized Diagnostic Tool for Thyroid Cancer Classification using Multi-view Ultrasound](<https://www.sota2.com/research/paper/68fa6ce11e209131cea3676e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-24T17:32:10.013Z

Authors: Han Huang, Yijie Dong, Xiaohong Jia, Jianqiao Zhou, Dong Ni, Jun Cheng, Ruobing Huang

Published: 2022-07-01T15:32:47.000Z

## Abstract

Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both transverse and longitudinal views using thyroid ultrasound. However, the appearance of the thyroid gland and lesions can vary dramatically across individuals. Identifying key diagnostic information from both views requires specialized expertise. Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds further difficulty to accurate diagnosis. To address these, we propose a personalized diagnostic tool that can customize its decision-making process for different patients. It consists of a multi-view classification module for feature extraction and a personalized weighting allocation network that generates optimal weighting for different views. It is also equipped with a self-supervised view-aware contrastive loss to further improve the model robustness towards different patient groups. Experimental results show that the proposed framework can better utilize multi-view information and outperform the competing methods.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Carotid Plaque Grading on Private Carotid US Images 1.0 (test)](<https://www.sota2.com/research/sota/carotid-plaque-grading-on-private-carotid-us-images-1-0-test.md>): Carotid plaque grading; Private Carotid US Images 1.0 (test); Accuracy: 89.36.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2207.00496>)

[PDF](<https://arxiv.org/pdf/2207.00496.pdf>)