GRIN+ is a new machine unlearning framework that targets fast and precise data erasure in imbalanced medical datasets. It separates unlearning‑specific knowledge from general representations by analyzing gradient contributions of forget and retain sets, introduces a class‑adaptive influence scoring to counter gradient dominance, and uses a direction‑constrained update to protect essential clinical knowledge. Benchmarks on skin cancer, brain tumor, and breast ultrasound data show that GRIN+ balances privacy, efficiency, and utility, achieving high diagnostic accuracy and faster runtime than existing methods.
By Minghui Huang, Junxiao Wang
ICON Decomposition is a new method for explaining deep neural networks by quantifying how much variance each concept explains in a network layer after accounting for all other concepts and the outcome. Unlike previous concept‑based methods that evaluate concepts in isolation, ICON can distinguish genuine model reliance from spurious correlations. Experiments on synthetic data, skin‑lesion, and brain‑imaging models show that ICON recovers concept importance more accurately, isolates truly relied‑upon concepts, and provides sparse explanations validated through retraining and out‑of‑distribution testing.
By Roshan Prakash Rane, Marco Simnacher, Manuel Pfeuffer, Marc-Andre Schulz, Nys Tjade Siegel, Maximilian Dreyer, Frederik Pahde, Wojciech Samek, Sonja Greven, Kerstin Ritter
Fed-ReMasker is a federated learning approach that adapts the ReMasker masked autoencoder for tabular data imputation, specifically addressing feature-level missingness where entire features are absent at some centers. The method enables centers to impute unobserved features by leveraging knowledge from collaborating institutions. In benchmark tests on synthetic and real-world datasets, Fed-ReMasker achieves the lowest imputation error in the majority of scenarios and remains robust to client heterogeneity, closely matching the performance of a centralized model.
By Ioannis Papathanail, Rooholla Poursoleymani, Lubnaa Abdur Rahman, Stavroula Georgia Mougiakakou
arXiv:2607. 06633v1 Announce Type: cross Abstract: In this paper, we address the problem of multimodal federated learning with missing modality.
By Aavash Chhetri, Bibek Niroula, Eduard Vazquez, Yash Raj Shrestha, Prashnna Gyawali, Loris Bazzani, Binod Bhattarai
The paper introduces a four-step pipeline that mines decision rules in the latent space of an FT-Transformer and then translates those rules back into measurable clinical features. By treating embedding dimensions that separate patient groups as latent biomarkers, small decision trees are used to extract rules, which are then mapped to raw features using gradient-input saliency and CLS attention attribution. Across six public clinical datasets, the translated rules generally outperformed raw-feature rules, achieving significant AUROC gains, though some high-performing latent rules could not be fully captured by simple raw-feature conditions.
By Majid Lotfian Delouee, Hamed Ayoobi, Sjors G. J. G. In 't Veld, Martijn C. Schut
arXiv:2608. 14657v1 Announce Type: new Abstract: Early identification of lung cancer risk is critical for timely intervention, yet existing prediction models are limited by their reliance on single data modalities and their inability to leverage structured clinical knowledge.
By Chunlei Yang, Shuyan Li, Zhong Cao