Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading

• URL: http://arxiv.org/abs/2302.06089v5
• Date: Thu, 28 Mar 2024 18:31:28 GMT
• Title: Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
• Authors: Fei Kong, Xiyue Wang, Jinxi Xiang, Sen Yang, Xinran Wang, Meng Yue, Jun Zhang, Junhan Zhao, Xiao Han, Yuhan Dong, Biyue Zhu, Fang Wang, Yueping Liu,
• Abstract summary: This study introduces a federated attention-consistent learning framework to address challenges associated with large-scale pathological images. We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole-slide images of prostate cancer from multiple centers.
• Score: 23.911710601714162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data. This study introduces a federated attention-consistent learning (FACL) framework to address challenges associated with large-scale pathological images and data heterogeneity. FACL enhances model generalization by maximizing attention consistency between local clients and the server model. To ensure privacy and validate robustness, we incorporated differential privacy by introducing noise during parameter transfer. We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole-slide images of prostate cancer from multiple centers. In the diagnosis task, FACL achieved an area under the curve (AUC) of 0.9718, outperforming seven centers with an average AUC of 0.9499 when categories are relatively balanced. For the Gleason grading task, FACL attained a Kappa score of 0.8463, surpassing the average Kappa score of 0.7379 from six centers. In conclusion, FACL offers a robust, accurate, and cost-effective AI training model for prostate cancer pathology while maintaining effective data safeguards.

Related papers

• Efficient endometrial carcinoma screening via cross-modal synthesis and gradient distillation [15.277910275783187]
  Early detection of myometrial invasion is critical for the staging and life-saving management of endometrial carcinoma (EC)<n>Here we present an automated, highly efficient two-stage deep learning framework that resolves both data and computational bottlenecks in EC screening.<n>Our model achieves a sensitivity of 99.5%, a specificity of 97.2%, and an area under the curve of 0.987 at a minimal computational cost.
  arXiv  Detail & Related papers  (2026-02-23T13:22:25Z)
• FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation [63.7829089874007]
  This paper introduces the Fetal Ultrasound Grand Challenge (FUGC), the first benchmark for semi-supervised learning in cervical segmentation.<n>FUGC provides a dataset of 890 TVS images, including 500 training images, 90 validation images, and 300 test images.<n> Methods were evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and runtime (RT), with a weighted combination of 0.4/0.4/0.2.
  arXiv  Detail & Related papers  (2026-01-22T01:34:39Z)
• Colorectal Cancer Histopathological Grading using Multi-Scale Federated Learning [0.0]
  We propose a scalable, privacy-preserving federated learning framework for colorectal cancer grading.<n>Our framework achieves an overall accuracy of 83.5%, outperforming a comparable centralized model.<n>The proposed modular pipeline establishes a foundational step toward deployable, privacy-aware clinical AI for digital pathology.
  arXiv  Detail & Related papers  (2025-11-05T18:18:09Z)
• An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection [55.35661671061754]
  Tuberculosis remains a critical global health issue, particularly in resource-limited and remote areas.<n>We propose a framework which enhances disease and symptom detection on chest X-rays by integrating two supervised heads and a self-supervised head.<n>Our model achieves an accuracy of 98.85% for distinguishing between COVID-19, tuberculosis, and normal cases, and a macro-F1 score of 90.09% for multilabel symptom detection.
  arXiv  Detail & Related papers  (2025-10-21T17:18:55Z)
• A Disease-Centric Vision-Language Foundation Model for Precision Oncology in Kidney Cancer [54.58205672910646]
  RenalCLIP is a visual-language foundation model for characterization, diagnosis and prognosis of renal mass.<n>It achieved better performance and superior generalizability across 10 core tasks spanning the full clinical workflow of kidney cancer.
  arXiv  Detail & Related papers  (2025-08-22T17:48:19Z)
• A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler [49.03919553747297]
  We propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries.<n>No prior studies have explored AI-driven cerebrovascular segmentation using Transcranial Color-coded Doppler (TCCD)<n>The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels.
  arXiv  Detail & Related papers  (2025-08-19T14:41:22Z)
• Congenital Heart Disease Classification Using Phonocardiograms: A Scalable Screening Tool for Diverse Environments [34.10187730651477]
  Congenital heart disease (CHD) is a critical condition that demands early detection. This study presents a deep learning model designed to detect CHD using phonocardiogram (PCG) signals. We evaluated our model on several datasets, including the primary dataset from Bangladesh.
  arXiv  Detail & Related papers  (2025-03-28T05:47:44Z)
• TRUSWorthy: Toward Clinically Applicable Deep Learning for Confident Detection of Prostate Cancer in Micro-Ultrasound [3.8208601340697386]
  We propose TRUSWorthy, a carefully designed, tuned, and integrated system for reliable PCa detection. Our pipeline integrates self-supervised learning, multiple-instance learning aggregation using transformers, random-undersampled boosting and ensembling. Our method outperforms previous state-of-the-art deep learning methods in terms of accuracy and uncertainty calibration.
  arXiv  Detail & Related papers  (2025-02-20T16:31:24Z)
• Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence [83.02106623401885]
  We present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries. It achieves an average area under the receiver operating characteristic curve of 0.927 for disease diagnosis and a dice similarity coefficient of 0.878 for lesion segmentation.
  arXiv  Detail & Related papers  (2024-11-25T13:40:11Z)
• Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training [3.2646075700744928]
  Histo whole slide images (WSIs) are being widely used to develop deep learning-based diagnostic solutions, especially for precision oncology. Most of these diagnostic softwares are vulnerable to biases and impurities in the training and test data which can lead to inaccurate diagnoses. We introduce HistoROI, a robust yet lightweight deep learning-based classifier to segregate WSI into six broad tissue regions.
  arXiv  Detail & Related papers  (2024-09-29T07:08:45Z)
• Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database [1.5186937600119894]
  Heart failure affects millions of people worldwide, significantly reducing quality of life and leading to high mortality rates. Despite extensive research, the relationship between heart failure and mortality rates among ICU patients is not fully understood. This study analyzed data from 1,177 patients over 18 years old from the MIMIC-III database, identified using ICD-9 codes.
  arXiv  Detail & Related papers  (2024-09-03T07:57:08Z)
• PrivFED -- A Framework for Privacy-Preserving Federated Learning in Enhanced Breast Cancer Diagnosis [0.0]
  This study introduces a federated learning framework, trained on the Wisconsin dataset, to mitigate challenges such as data scarcity and imbalance. The model exhibits an average accuracy of 99.95% on edge devices and 98% on the central server.
  arXiv  Detail & Related papers  (2024-05-13T18:01:57Z)
• Empowering Healthcare through Privacy-Preserving MRI Analysis [3.6394715554048234]
  We introduce the Ensemble-Based Federated Learning (EBFL) Framework. EBFL framework deviates from the conventional approach by emphasizing model features over sharing sensitive patient data. We have achieved remarkable precision in the classification of brain tumors, including glioma, meningioma, pituitary, and non-tumor instances.
  arXiv  Detail & Related papers  (2024-03-14T19:51:18Z)
• OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detection [2.3349787245442966]
  Our research contributes a self-supervised robust machine learning framework, OCT-SelfNet, for detecting eye diseases. Our method addresses the issue using a two-phase training approach that combines self-supervised pretraining and supervised fine-tuning. In terms of the AUC-PR metric, our proposed method exceeded 42%, showcasing a substantial increase of at least 10% in performance compared to the baseline.
  arXiv  Detail & Related papers  (2024-01-22T20:17:14Z)
• A Federated Learning Framework for Stenosis Detection [70.27581181445329]
  This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA) Two heterogeneous datasets from two institutions were considered: dataset 1 includes 1219 images from 200 patients, which we acquired at the Ospedale Riuniti of Ancona (Italy) dataset 2 includes 7492 sequential images from 90 patients from a previous study available in the literature.
  arXiv  Detail & Related papers  (2023-10-30T11:13:40Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis [48.64462717254158]
  We developed a self-supervised contrastive learning approach, EchoCLR, to catered to echocardiogram videos. When fine-tuned on small portions of labeled data, EchoCLR pretraining significantly improved classification performance for left ventricular hypertrophy (LVH) and aortic stenosis (AS) EchoCLR is unique in its ability to learn representations of medical videos and demonstrates that SSL can enable label-efficient disease classification from small, labeled datasets.
  arXiv  Detail & Related papers  (2022-07-23T19:17:26Z)
• Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence [79.038671794961]
  We launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK.
  arXiv  Detail & Related papers  (2021-11-18T00:43:41Z)

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.