Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …
medical image analysis, potentially improving healthcare and patient outcomes. However …
Explainable, domain-adaptive, and federated artificial intelligence in medicine
Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …
each domain is driven by a growing body of annotated data, increased computational …
Fedseg: Class-heterogeneous federated learning for semantic segmentation
Federated Learning (FL) is a distributed learning paradigm that collaboratively learns a
global model across multiple clients with data privacy-preserving. Although many FL …
global model across multiple clients with data privacy-preserving. Although many FL …
Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging
The collection and curation of large-scale medical datasets from multiple institutions is
essential for training accurate deep learning models, but privacy concerns often hinder data …
essential for training accurate deep learning models, but privacy concerns often hinder data …
Memory-aware curriculum federated learning for breast cancer classification
Abstract Background and Objective: For early breast cancer detection, regular screening
with mammography imaging is recommended. Routine examinations result in datasets with …
with mammography imaging is recommended. Routine examinations result in datasets with …
Distributed contrastive learning for medical image segmentation
Supervised deep learning needs a large amount of labeled data to achieve high
performance. However, in medical imaging analysis, each site may only have a limited …
performance. However, in medical imaging analysis, each site may only have a limited …
Federated learning for medical image analysis: A survey
Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …
the small sample size problem. Many recent studies suggest using multi-domain data …
FedMix: Mixed supervised federated learning for medical image segmentation
The purpose of federated learning is to enable multiple clients to jointly train a machine
learning model without sharing data. However, the existing methods for training an image …
learning model without sharing data. However, the existing methods for training an image …
Federated cycling (FedCy): Semi-supervised Federated Learning of surgical phases
H Kassem, D Alapatt, P Mascagni… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Recent advancements in deep learning methods bring computer-assistance a step closer to
fulfilling promises of safer surgical procedures. However, the generalizability of such …
fulfilling promises of safer surgical procedures. However, the generalizability of such …
Dissecting self-supervised learning methods for surgical computer vision
The field of surgical computer vision has undergone considerable breakthroughs in recent
years with the rising popularity of deep neural network-based methods. However, standard …
years with the rising popularity of deep neural network-based methods. However, standard …