# Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos

arXiv: https://arxiv.org/abs/2206.13318 · HTML: https://mlsanity.com/paper/2206.13318/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 27 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Grounded Video Description](https://mlsanity.com/paper/1812.06587/) (2018)
- [U-LanD: Uncertainty-Driven Video Landmark Detection](https://mlsanity.com/paper/2102.01586/) (2021)

## Built on by (most cited first, 8 of 8)

- [Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers](https://mlsanity.com/paper/2306.06877/) (2023)
- [Breast Lesion Diagnosis Using Static Images and Dynamic Video](https://mlsanity.com/paper/2308.09980/) (2023)
- [Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound](https://mlsanity.com/paper/2506.23538/) (2025)
- [UMind-VL: A Generalist Ultrasound Vision-Language Model for Unified Grounded Perception and Comprehensive Interpretation](https://mlsanity.com/paper/2511.22256/) (2025)
- [Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection](https://mlsanity.com/paper/2305.18060/) (2023)
- [UltraG-Bench: A Multi-task Benchmark for assessing Large Vision-Language Models on Pixel-level Evidence Grounding in Ultrasound](https://mlsanity.com/paper/2609.30928/) (2026)
- [A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans](https://mlsanity.com/paper/2506.23209/) (2025)
- [A Fully Automatic Framework for Intracranial Pressure Grading: Integrating Keyframe Identification, ONSD Measurement and Clinical Data](https://mlsanity.com/paper/2509.09368/) (2025)

## Abstract (arXiv)

Ultrasound examination is widely used in the clinical diagnosis of thyroid nodules (benign/malignant). However, the accuracy relies heavily on radiologist experience. Although deep learning techniques have been investigated for thyroid nodules recognition. Current solutions are mainly based on static ultrasound images, with limited temporal information used and inconsistent with clinical diagnosis. This paper proposes a novel method for the automated recognition of thyroid nodules through an exhaustive exploration of ultrasound videos and key-frames. We first propose a detection-localization framework to automatically identify the clinical key-frame with a typical nodule in each ultrasound video. Based on the localized key-frame, we develop a key-frame guided video classification model for thyroid nodule recognition. Besides, we introduce a motion attention module to help the network focus on significant frames in an ultrasound video, which is consistent with clinical diagnosis. The proposed thyroid nodule recognition framework is validated on clinically collected ultrasound videos, demonstrating superior performance compared with other state-of-the-art methods.