Machine Learning and Vision Transformers for Thyroid Carcinoma Diagnosis: A review

• URL: http://arxiv.org/abs/2403.13843v1
• Date: Sun, 17 Mar 2024 17:45:04 GMT
• Title: Machine Learning and Vision Transformers for Thyroid Carcinoma Diagnosis: A review
• Authors: Yassine Habchi, Hamza Kheddar, Yassine Himeur, Abdelkrim Boukabou, Ammar Chouchane, Abdelmalik Ouamane, Shadi Atalla, Wathiq Mansoor,
• Abstract summary: This review article presents a summary of various studies on AIbased approaches, especially those employing transformers, for diagnosing thyroid cancer. It introduces a new categorization system for these methods based on artifcial intelligence (AI) algorithms, the goals of the framework, and the computing environments used. The paper highlights the importance of AI instruments in aiding the diagnosis and treatment of TC through supervised, unsupervised, or mixed approaches.
• Score: 3.2071249735671348
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 AIbased approaches, especially those employing transformers, for diagnosing TC. It introduces a new categorization system for these methods based on artifcial 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 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 feld.

Related papers

• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  This study focuses on the clinical evaluation of medical Synthetic Data Generation using Artificial Intelligence (AI) models. The paper contributes by a) presenting a protocol for the systematic evaluation of synthetic images by medical experts and b) applying it to assess TIDE-II, a novel variational autoencoder-based model for high-resolution WCE image synthesis. The results show that TIDE-II generates clinically relevant WCE images, helping to address data scarcity and enhance diagnostic tools.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques [38.321248253111776]
  Article explores the application of Explainable Artificial Intelligence (XAI) techniques in the detection and diagnosis of breast cancer. Aims to highlight the potential of XAI in bridging the gap between complex AI models and practical healthcare applications.
  arXiv  Detail & Related papers  (2024-06-01T18:50:03Z)
• Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI [0.0]
  The study presents an integrated framework combining Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XAI) for the enhanced diagnosis of breast cancer. The methodology encompasses an elaborate data preprocessing pipeline and advanced data augmentation techniques to counteract dataset limitations. A focal point of our study is the evaluation of XAI's effectiveness in interpreting model predictions.
  arXiv  Detail & Related papers  (2024-04-05T05:00:21Z)
• Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data [0.05025737475817938]
  We propose a modular machine learning framework designed for personalized counterfactual cancer treatment suggestions. The framework is tailored to address critical challenges inherent in data-driven cancer research. Our method aims to empower clinicians with a reality-centric decision-support tool.
  arXiv  Detail & Related papers  (2024-02-19T14:54:20Z)
• AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions [3.2071249735671348]
  This review paper summarizes a large collection of articles related to artificial intelligence (AI)-based techniques used in the diagnosis of thyroid cancer. The focus of this study is on how AI-based tools can support the diagnosis and treatment of thyroid cancer, through supervised, unsupervised, or hybrid techniques.
  arXiv  Detail & Related papers  (2023-08-25T17:27:53Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT [41.199716328468895]
  We develop a dual-path transformer to exploit the feasibility of classification and segmentation of pancreatic lesions. The proposed method consists of a CNN-based segmentation path (S-path) and a transformer-based classification path (C-path) Our results show that our method can enable accurate classification and segmentation of the full taxonomy of pancreatic lesions.
  arXiv  Detail & Related papers  (2023-03-02T03:34:28Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• Deep Learning in Computer-Aided Diagnosis and Treatment of Tumors: A Survey [42.16618852663992]
  Computer-Aided Diagnosis and Treatment of Tumors is a hot topic of deep learning in recent years. This survey presents the applications of deep learning in the Computer-Aided Diagnosis and Treatment of Tumors.
  arXiv  Detail & Related papers  (2020-11-02T12:42:19Z)
• Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy [7.840459682652335]
  The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems. There are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. EndoCV 2020 challenges are designed to address research questions in these remits.
  arXiv  Detail & Related papers  (2020-10-12T21:22:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.