# Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review

arXiv: https://arxiv.org/abs/2403.13843 · HTML: https://mlsanity.com/paper/2403.13843/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 278 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A high-bias, low-variance introduction to Machine Learning for physicists](https://mlsanity.com/paper/1803.08823/) (2018)
- [XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models](https://mlsanity.com/paper/2306.07971/) (2023)
- [Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey](https://mlsanity.com/paper/2403.01255/) (2024)
- [A survey of advances in vision-based vehicle re-identification](https://mlsanity.com/paper/1905.13258/) (2019)
- [Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization](https://mlsanity.com/paper/2304.14535/) (2023)
- [Image Data Augmentation Approaches: A Comprehensive Survey and Future directions](https://mlsanity.com/paper/2301.02830/) (2023)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [Panoptic Segmentation: A Review](https://mlsanity.com/paper/2111.10250/) (2021)
- [Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions](https://mlsanity.com/paper/2307.14107/) (2023)
- [AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions](https://mlsanity.com/paper/2308.13592/) (2023)
- [Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions](https://mlsanity.com/paper/2308.04522/) (2023)
- [Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging](https://mlsanity.com/paper/2308.06534/) (2023)
- [Survey of XAI in digital pathology](https://mlsanity.com/paper/2008.06353/) (2020)
- [A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects](https://mlsanity.com/paper/2102.07654/) (2021)
- [Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review](https://mlsanity.com/paper/2307.01221/) (2023)
- [Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research](https://mlsanity.com/paper/1909.00311/) (2019)
- [The emergence of Explainability of Intelligent Systems: Delivering Explainable and Personalised Recommendations for Energy Efficiency](https://mlsanity.com/paper/2010.04990/) (2020)
- [Intelligent edge-based recommender system for internet of energy applications](https://mlsanity.com/paper/2111.13272/) (2021)
- [Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images](https://mlsanity.com/paper/1904.00839/) (2019)
- [Improving CNN-based Person Re-identification using score Normalization](https://mlsanity.com/paper/2307.00397/) (2023)
- [Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection](https://mlsanity.com/paper/2311.09942/) (2023)
- [Hybrid Whale-Mud-Ring Optimization for Precise Color Skin Cancer Image Segmentation](https://mlsanity.com/paper/2311.13512/) (2023)
- [Extracting Thyroid Nodules Characteristics from Ultrasound Reports Using Transformer-based Natural Language Processing Methods](https://mlsanity.com/paper/2304.00115/) (2023)
- [AI-Generated Content Enhanced Computer-Aided Diagnosis Model for Thyroid Nodules: A ChatGPT-Style Assistant](https://mlsanity.com/paper/2402.02401/) (2024)

## Built on by (most cited first, 2 of 2)

- [Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems](https://mlsanity.com/paper/2405.18624/) (2024)
- [Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models](https://mlsanity.com/paper/2501.13962/) (2025)

## Abstract (arXiv)

The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable. In particular, the challenge of identifying thyroid cancer (TC) has seen progress with the use of machine learning (ML) and big data analysis, incorporating Transformers to evaluate TC prognosis and determine the risk of malignancy in individuals. This review article presents a summary of various studies on AI-based approaches, especially those employing Transformers, for diagnosing TC. It introduces a new categorization system for these methods based on artificial intelligence (AI) algorithms, the goals of the framework, and the computing environments used. Additionally, it scrutinizes and contrasts the available TC datasets by their features. The paper highlights the importance of AI instruments in aiding the diagnosis and treatment of TC through supervised, unsupervised, or mixed approaches, with a special focus on the ongoing importance of Transformers and large language models (LLMs) in medical diagnostics and disease management. It further discusses the progress made and the continuing obstacles in this area. Lastly, it explores future directions and focuses within this research field.