Explainable Federated Medical Image Analysis Through Causal Learning and Blockchain.

Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhead and data heterogeneity. This paper proposes novel FL techniques using explainable artificial intelligence (XAI) for efficient, accurate, and trustworthy analysis.

ARPruning: An automatic channel pruning based on attention map ranking.

Structured pruning is a representative model compression technology for convolutional neural networks (CNNs), aiming to prune some less important filters or channels of CNNs. Most recent structured pruning methods have established some criteria to measure the importance of filters, which are mainly based on the magnitude of weights or other parameters in CNNs. However, these judgment criteria lack explainability, and it is insufficient to simply rely on the numerical values of the network parameters to assess the relationship between the channel and the model performance.

Non-differentiable saddle points and sub-optimal local minima exist for deep ReLU networks.

Whether sub-optimal local minima and saddle points exist in the highly non-convex loss landscape of deep neural networks has a great impact on the performance of optimization algorithms. Theoretically, we study in this paper the existence of non-differentiable sub-optimal local minima and saddle points for deep ReLU networks with arbitrary depth. We prove that there always exist non-differentiable saddle points in the loss surface of deep ReLU networks with squared loss or cross-entropy loss under reasonable assumptions.

[hFgl2 protein expression in peripheral blood mononuclear cells in different clinical types of liver disease].

Objective: To detect hFgl2 expression in peripheral blood mononuclear cells in patients with chronic hepatitis B and liver cancer and explore its association with the severity of chronic hepatitis B.

Methods: The protein expression of hFgl2 in peripheral blood mononuclear cells was detected in 78 patients with chronic hepatitis B (including mild, moderate, or severe cases), chronic severe hepatitis, or liver cancer, with 20 healthy volunteers as controls. The data were analyzed in comparison with the patients' alanine aminotransferase (ALT), aspartate aminotransferase (AST) and total bilirubin (TBiL and) levels.