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Jeonbuk National University

Academic institutionasia · kr
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Research library20linked papers
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Selected work

Representative Papers

ProBAG: Prototype-Guided Boundary-Aware Graph Diffusion for Weakly Supervised Histopathology Segmentation

Aug 12, 2026

This work addresses the limitations of conventional Class Activation Mapping (CAM) approaches in weakly supervised semantic segmentation of histopathology images using only image-level labels—specifically, their tendency to produce blurry boundaries and over-localize on highly discriminative regions. To overcome these issues, the authors propose ProBAG, a novel method that integrates pathology-aligned CONCH text prototypes with multi-scale frozen UNI visual features to generate high-quality pseudo-masks. ProBAG further introduces class-wise power recalibration to preserve total foreground activation and designs an attention-based contextual discrepancy-driven graph diffusion mechanism to enhance boundary awareness without relying on CRF or external models. Integrated within a Phikon-FPN two-stage segmentation framework, ProBAG achieves state-of-the-art performance on BCSS-WSSS and LUAD-HistoSeg benchmarks. Ablation studies confirm that pathological text semantics provide the largest contribution, while graph diffusion offers complementary gains.

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CoDiR: Confidence-Guided Diffusion Refinement for Semi-Supervised Histopathology Segmentation

Aug 12, 2026

This work addresses the challenges of scarce annotations and unreliable pseudo-labels in ambiguous gland regions for semi-supervised histopathology image segmentation. To this end, we propose a confidence-guided diffusion refinement mechanism built upon the Mean Teacher framework. In regions where the teacher model exhibits low prediction confidence, a conditional diffusion model is introduced to perform structure-aware refinement, while high-confidence predictions are leveraged to formulate a weighted consistency loss for training the student model. The proposed approach substantially enhances pseudo-label quality, achieving mDice scores of 88.09%/89.83% on the GlaS dataset and 89.19%/90.29% on the CRAG dataset using only 10% and 20% labeled data, respectively. Notably, the diffusion refinement module alone contributes a +6.36% mDice improvement, consistently outperforming current state-of-the-art methods.

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MIFA: An MILP-based Framework for Improving Differential Fault Attacks

Aug 07, 2026

This work addresses the inefficiency of differential fault analysis (DFA) by proposing the first DFA framework based on mixed-integer linear programming (MILP). For the first time, MILP is employed to systematically search for differential trails with a unique solution, combined with bit-level single-bit flip fault modeling to optimize both the location and number of injected faults. The approach enables attacks on deeper-round implementations and allows theoretical computation of the minimal number of faults required to recover the secret key. When applied to the DEFAULT block cipher, the method uniquely recovers the full key with only three faults in the sixth-to-last round and two faults each in the seventh- and eighth-to-last rounds, significantly outperforming existing DFA results and effectively breaking the cipher’s claimed DFA resistance.

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Recent publications

Latest Papers

ProBAG: Prototype-Guided Boundary-Aware Graph Diffusion for Weakly Supervised Histopathology Segmentation

Aug 12, 2026

This work addresses the limitations of conventional Class Activation Mapping (CAM) approaches in weakly supervised semantic segmentation of histopathology images using only image-level labels—specifically, their tendency to produce blurry boundaries and over-localize on highly discriminative regions. To overcome these issues, the authors propose ProBAG, a novel method that integrates pathology-aligned CONCH text prototypes with multi-scale frozen UNI visual features to generate high-quality pseudo-masks. ProBAG further introduces class-wise power recalibration to preserve total foreground activation and designs an attention-based contextual discrepancy-driven graph diffusion mechanism to enhance boundary awareness without relying on CRF or external models. Integrated within a Phikon-FPN two-stage segmentation framework, ProBAG achieves state-of-the-art performance on BCSS-WSSS and LUAD-HistoSeg benchmarks. Ablation studies confirm that pathological text semantics provide the largest contribution, while graph diffusion offers complementary gains.

0 citationsRead paper

CoDiR: Confidence-Guided Diffusion Refinement for Semi-Supervised Histopathology Segmentation

Aug 12, 2026

This work addresses the challenges of scarce annotations and unreliable pseudo-labels in ambiguous gland regions for semi-supervised histopathology image segmentation. To this end, we propose a confidence-guided diffusion refinement mechanism built upon the Mean Teacher framework. In regions where the teacher model exhibits low prediction confidence, a conditional diffusion model is introduced to perform structure-aware refinement, while high-confidence predictions are leveraged to formulate a weighted consistency loss for training the student model. The proposed approach substantially enhances pseudo-label quality, achieving mDice scores of 88.09%/89.83% on the GlaS dataset and 89.19%/90.29% on the CRAG dataset using only 10% and 20% labeled data, respectively. Notably, the diffusion refinement module alone contributes a +6.36% mDice improvement, consistently outperforming current state-of-the-art methods.

0 citationsRead paper

MIFA: An MILP-based Framework for Improving Differential Fault Attacks

Aug 07, 2026

This work addresses the inefficiency of differential fault analysis (DFA) by proposing the first DFA framework based on mixed-integer linear programming (MILP). For the first time, MILP is employed to systematically search for differential trails with a unique solution, combined with bit-level single-bit flip fault modeling to optimize both the location and number of injected faults. The approach enables attacks on deeper-round implementations and allows theoretical computation of the minimal number of faults required to recover the secret key. When applied to the DEFAULT block cipher, the method uniquely recovers the full key with only three faults in the sixth-to-last round and two faults each in the seventh- and eighth-to-last rounds, significantly outperforming existing DFA results and effectively breaking the cipher’s claimed DFA resistance.

0 citationsRead paper