Deep Transfer Learning for Kidney Cancer Diagnosis

• URL: http://arxiv.org/abs/2408.04318v1
• Date: Thu, 8 Aug 2024 08:52:29 GMT
• Title: Deep Transfer Learning for Kidney Cancer Diagnosis
• Authors: Yassine Habchi, Hamza Kheddar, Yassine Himeur, Abdelkrim Boukabou, Shadi Atalla, Wathiq Mansoor, Hussain Al-Ahmad,
• Abstract summary: Transfer learning (TL) has been proposed that can produce impressive results based on other different pre-trained data. This paper presents, to the best of the authors' knowledge, the first comprehensive survey of DL-based TL frameworks for kidney cancer diagnosis.
• Score: 2.87932876218736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many incurable diseases prevalent across global societies stem from various influences, including lifestyle choices, economic conditions, social factors, and genetics. Research predominantly focuses on these diseases due to their widespread nature, aiming to decrease mortality, enhance treatment options, and improve healthcare standards. Among these, kidney disease stands out as a particularly severe condition affecting men and women worldwide. Nonetheless, there is a pressing need for continued research into innovative, early diagnostic methods to develop more effective treatments for such diseases. Recently, automatic diagnosis of Kidney Cancer has become an important challenge especially when using deep learning (DL) due to the importance of training medical datasets, which in most cases are difficult and expensive to obtain. Furthermore, in most cases, algorithms require data from the same domain and a powerful computer with efficient storage capacity. To overcome this issue, a new type of learning known as transfer learning (TL) has been proposed that can produce impressive results based on other different pre-trained data. This paper presents, to the best of the authors' knowledge, the first comprehensive survey of DL-based TL frameworks for kidney cancer diagnosis. This is a strong contribution to help researchers understand the current challenges and perspectives of this topic. Hence, the main limitations and advantages of each framework are identified and detailed critical analyses are provided. Looking ahead, the article identifies promising directions for future research. Moving on, the discussion is concluded by reflecting on the pivotal role of TL in the development of precision medicine and its effects on clinical practice and research in oncology.

Related papers

• The Role of Explainable AI in Revolutionizing Human Health Monitoring [0.0]
  Explainable AI (XAI) offers greater clarity and has the potential to significantly improve patient care. This literature review focuses on chronic conditions such as Parkinson's, stroke, depression, cancer, heart disease, and Alzheimer's disease. The article is concluded with a critical appraisal of the challenges and future research opportunities for XAI in human health monitoring.
  arXiv  Detail & Related papers  (2024-09-11T15:31:40Z)
• Assessing and Enhancing Large Language Models in Rare Disease Question-answering [64.32570472692187]
  We introduce a rare disease question-answering (ReDis-QA) dataset to evaluate the performance of Large Language Models (LLMs) in diagnosing rare diseases. We collected 1360 high-quality question-answer pairs within the ReDis-QA dataset, covering 205 rare diseases. We then benchmarked several open-source LLMs, revealing that diagnosing rare diseases remains a significant challenge for these models. Experiment results demonstrate that ReCOP can effectively improve the accuracy of LLMs on the ReDis-QA dataset by an average of 8%.
  arXiv  Detail & Related papers  (2024-08-15T21:09:09Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• From Data to Insights: A Comprehensive Survey on Advanced Applications in Thyroid Cancer Research [18.42107238058712]
  We conducted a systematic review and developed a comprehensive taxonomy of machine learning-based applications in thyroid cancer. A total of 758 related studies were identified and scrutinized. We highlight key challenges encountered in this domain and propose future research opportunities.
  arXiv  Detail & Related papers  (2024-01-08T08:10:37Z)
• Deep Neural Decision Forest: A Novel Approach for Predicting Recovery or Decease of Patients [1.0874223087191939]
  This study aims to examine whether deep learning algorithms can predict a patient's morality. We investigated the impact of Clinical and RT-PCR on prediction to determine which one is more reliable. Results indicate that Clinical alone (without the use of RT-PCR) is the most effective method of diagnosis, with an accuracy of 80%.
  arXiv  Detail & Related papers  (2023-11-23T11:21:40Z)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data [66.43957431843324]
  We introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization.
  arXiv  Detail & Related papers  (2022-04-24T07:38:37Z)
• Machine learning based disease diagnosis: A comprehensive review [0.0]
  This review explains how Machine Learning (ML) and Deep Learning (DL) are being used to help in the early identification of numerous diseases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in Machine Learning-based Disease Diagnosis (MLBDD)
  arXiv  Detail & Related papers  (2021-12-31T16:25:23Z)
• Heterogeneity Loss to Handle Intersubject and Intrasubject Variability in Cancer [11.440201348567681]
  Deep learning (DL) models have shown impressive results in medical domain. These AI methods can provide immense support to developing nations as affordable healthcare solutions. This work is focused on one such application of blood cancer diagnosis.
  arXiv  Detail & Related papers  (2020-03-06T16:16:23Z)

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.