# Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification

arXiv: https://arxiv.org/abs/2005.14117 · HTML: https://mlsanity.com/paper/2005.14117/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 10 of 10)

- [Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation](https://mlsanity.com/paper/2404.14852/) (2024)
- [Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition](https://mlsanity.com/paper/2304.11275/) (2023)
- [Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis](https://mlsanity.com/paper/2207.00807/) (2022)
- [3D Hand Pose and Shape Estimation from RGB Images for Keypoint-Based Hand Gesture Recognition](https://mlsanity.com/paper/2109.13879/) (2021)
- [Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals](https://mlsanity.com/paper/2203.05864/) (2022)
- [Analyzing EEG Data with Machine and Deep Learning: A Benchmark](https://mlsanity.com/paper/2203.10009/) (2022)
- [Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application](https://mlsanity.com/paper/2203.14031/) (2022)
- [SIRe-Networks: Convolutional Neural Networks Architectural Extension for Information Preservation via Skip/Residual Connections and Interlaced Auto-Encoders](https://mlsanity.com/paper/2110.02776/) (2021)
- [OliVaR: Improving Olive Variety Recognition using Deep Neural Networks](https://mlsanity.com/paper/2303.00431/) (2023)
- [IOTA: Corrective Knowledge-Guided Prompt Learning via Black-White Box Framework](https://mlsanity.com/paper/2601.20526/) (2026)

## Abstract (arXiv)

Computer-aided diagnosis (CAD) is becoming a prominent approach to assist clinicians spanning across multiple fields. These automated systems take advantage of various computer vision (CV) procedures, as well as artificial intelligence (AI) techniques, to formulate a diagnosis of a given image, e.g., computed tomography and ultrasound. Advances in both areas (CV and AI) are enabling ever increasing performances of CAD systems, which can ultimately avoid performing invasive procedures such as fine-needle aspiration. In this study, a novel end-to-end knowledge-driven classification framework is presented. The system focuses on multimodal data generated by thyroid ultrasonography, and acts as a CAD system by providing a thyroid nodule classification into the benign and malignant categories. Specifically, the proposed system leverages cues provided by an ensemble of experts to guide the learning phase of a densely connected convolutional network (DenseNet). The ensemble is composed by various networks pretrained on ImageNet, including AlexNet, ResNet, VGG, and others. The previously computed multimodal feature parameters are used to create ultrasonography domain experts via transfer learning, decreasing, moreover, the number of samples required for training. To validate the proposed method, extensive experiments were performed, providing detailed performances for both the experts ensemble and the knowledge-driven DenseNet. As demonstrated by the results, the proposed system achieves relevant performances in terms of qualitative metrics for the thyroid nodule classification task, thus resulting in a great asset when formulating a diagnosis.