A semi-supervised federated learning approach leveraging pseudo-labeling for Knee Osteoarthritis severity detection
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Date
2024-06Publisher
Brac UniversityAuthor
Rifat, Rakib HossainMetadata
Show full item recordAbstract
Within medical image analysis, appropriately classifying the extent of knee osteoarthritis
is a significant obstacle, made more difficult by the scarcity of annotated
data and strict privacy rules. Conventional approaches are hindered by the exorbitant
expenses, limited availability of annotated datasets, as well as issues over the
confidentiality of patient data. To overcome these challenges, we propose a method
which is a Federated Learning Framework that utilizes pseudo-labeling we are calling
it PLFL. Our innovative approach avoids the cost of human annotation and guarantees
patient confidentiality through Federated Learning while reducing the dangers
linked to adversarial assaults and annotation mistakes. Our proposed method works
under the assumption that the server is the only custodian of gold label data, while
the client side does not have any label data. The server utilizes gold-labeled data
to train the global model and subsequently applies the federated learning approach.
Clients add labels to unlabeled data by picking labels that meet or exceed a minimal
threshold level of confidence in the prediction. Once data on the client side reaches
the specified confidence score, it is added to the client’s dataset. Upon receiving the
labeled data, the client initiates the training process and sends the weight of the
local model. Subsequently, the server aggregates the weights of each model using the
FedAvg technique. The thorough assessment of our system, in comparison to the
standard client-server-based Federated Learning approach (CSFL) and FixMatchbased
semi-supervised Federated Learning (FSSFL) approach, clearly shows significant
performance improvements. Our framework PLFL showed superior performance
compared to other explained techniques, with consistent accuracy, weighted
average precision, recall, and an F1-score of 0.88. Significantly, it outperforms both
CSFL and FSSFL Frameworks, significantly enhancing model performance and efficiency.
The proposed framework achieves an accuracy of 93.07% for the healthy
class, 64.00% for the moderate class, and 100% for the severe class. Furthermore,
our system has exceptional prediction precision, especially in detecting moderate
and severe instances of osteoarthritis, surpassing rival frameworks. This is seen
in the notable progress in accurately forecasting moderate and severe categories,
highlighting the effectiveness of our method. The pseudo-labeling-based framework
had the shortest duration for label generation and model training, 3.2 times shorter
than the best-performing model of the traditional Federated Learning Framework
(CSFL) and 1.7 times lower than the best-performing model of the FixMatch-Based
Federated Learning Framework (FSSFL). This thesis proposes an innovative investigation
into identifying knee osteoarthritis severity, the first instance of applying
semi-supervised and federated learning approaches in this field. Our goal is to stimulate
progress in medical image analysis by using our innovative technique, resulting
in more precise diagnoses and better patient outcomes.