Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection

• URL: http://arxiv.org/abs/2504.13186v1
• Date: Sun, 30 Mar 2025 15:17:40 GMT
• Title: Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection
• Authors: Yassine Habchi, Hamza Kheddar, Yassine Himeur, Adel Belouchrani, Erchin Serpedin, Fouad Khelifi, Muhammad E. H. Chowdhury,
• Abstract summary: Deep learning (DL) has transformed healthcare, particularly in cancer detection and diagnosis.<n>Despite numerous reviews on DL in healthcare, a comprehensive analysis of its role in cancer detection remains limited.<n>This paper addresses these gaps by reviewing advanced DL techniques, including transfer learning (TL), reinforcement learning (RL), federated learning (FL), Transformers, and large language models (LLMs)
• Score: 5.428095624923599
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of deep learning (DL) has transformed healthcare, particularly in cancer detection and diagnosis. DL surpasses traditional machine learning and human accuracy, making it a critical tool for identifying diseases. Despite numerous reviews on DL in healthcare, a comprehensive analysis of its role in cancer detection remains limited. Existing studies focus on specific aspects, leaving gaps in understanding its broader impact. This paper addresses these gaps by reviewing advanced DL techniques, including transfer learning (TL), reinforcement learning (RL), federated learning (FL), Transformers, and large language models (LLMs). These approaches enhance accuracy, tackle data scarcity, and enable decentralized learning while maintaining data privacy. TL adapts pre-trained models to new datasets, improving performance with limited labeled data. RL optimizes diagnostic pathways and treatment strategies, while FL fosters collaborative model development without sharing sensitive data. Transformers and LLMs, traditionally used in natural language processing, are now applied to medical data for improved interpretability. Additionally, this review examines these techniques' efficiency in cancer diagnosis, addresses challenges like data imbalance, and proposes solutions. It serves as a resource for researchers and practitioners, providing insights into current trends and guiding future research in advanced DL for cancer detection.

Related papers

• Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey [43.75849983150303]
  As datasets become more complex, traditional anomaly detection methods struggle to capture intricate patterns.<n>Deep learning has made AD methods more powerful and adaptable, improving their ability to handle high-dimensional and unstructured data.<n>This review bridges gaps in existing literature and serves as a valuable resource for researchers and practitioners seeking to enhance AD techniques using deep learning.
  arXiv  Detail & Related papers  (2025-03-17T14:04:48Z)
• Deep Transfer Learning for Kidney Cancer Diagnosis [2.87932876218736]
  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.
  arXiv  Detail & Related papers  (2024-08-08T08:52:29Z)
• Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models [46.05020842978823]
  Large Language Models (LLMs) have emerged as powerful tools to navigate this complex data landscape. RAGGED is a comprehensive workflow designed to support investigators with knowledge integration and hypothesis generation.
  arXiv  Detail & Related papers  (2024-07-17T07:44:18Z)
• Federated and Transfer Learning for Cancer Detection Based on Image Analysis [2.696333064387343]
  This review article discusses the roles of federated learning (FL) and transfer learning (TL) in cancer detection based on image analysis. FL enables the training of machine learning models on data distributed across multiple sites without the need for centralized data sharing. TL allows for the transfer of knowledge from one task to another.
  arXiv  Detail & Related papers  (2024-05-30T15:07:30Z)
• Augmented Risk Prediction for the Onset of Alzheimer's Disease from Electronic Health Records with Large Language Models [42.676566166835585]
  Alzheimer's disease (AD) is the fifth-leading cause of death among Americans aged 65 and older. Recent advancements in large language models (LLMs) offer strong potential for enhancing risk prediction. This paper proposes a novel pipeline that augments risk prediction by leveraging the few-shot inference power of LLMs.
  arXiv  Detail & Related papers  (2024-05-26T03:05:10Z)
• Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review [2.392768534292846]
  This review article presents a summary of various studies on AI-based approaches, especially those employing Transformers, for diagnosing thyroid cancer (TC)<n>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.<n>The paper highlights the importance of AI instruments in aiding the diagnosis and treatment of TC through supervised, unsupervised, or mixed approaches.
  arXiv  Detail & Related papers  (2024-03-17T17:45:04Z)
• The Significance of Machine Learning in Clinical Disease Diagnosis: A Review [0.0]
  This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics. The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics.
  arXiv  Detail & Related papers  (2023-10-25T20:28:22Z)
• 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)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• Deep learning methods for drug response prediction in cancer: predominant and emerging trends [50.281853616905416]
  Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
  arXiv  Detail & Related papers  (2022-11-18T03:26:31Z)
• Dissecting Self-Supervised Learning Methods for Surgical Computer Vision [51.370873913181605]
  Self-Supervised Learning (SSL) methods have begun to gain traction in the general computer vision community. The effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection.
  arXiv  Detail & Related papers  (2022-07-01T14:17:11Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View [36.025217954247125]
  We introduce the methods for interpretability in depth and comprehensively as a methodological reference for future researchers or clinical practitioners. We discuss how these methods have been adapted and applied to healthcare problems and how they can help physicians better understand these data-driven technologies.
  arXiv  Detail & Related papers  (2021-12-05T17:12:53Z)
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning [49.86828302591469]
  We train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features.
  arXiv  Detail & Related papers  (2020-04-30T20:42:17Z)

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.