arXiv subjects

Zhonghua Xie

Publications and source records attributed to Zhonghua Xie.

3 recordsLinked to original sources

arXiv2026

LatticeRank: Spacing-Ranked Consistency for Native-Grid 3D Medical Image Segmentation

Variable voxel spacing changes more than image resolution: it alters the discrete sampling lattice, tensor geometry, and anatomical evidence available to a 3D segmentation network. While conventional pipelines restore images to a canonical grid, direct-grid deployment requires the model itself to tolerate these changes. We observe that candidate grids are not exchangeable views -- a coarse grid removes more structural information than a mildly resampled one -- yet standard consistency objectives assign them equal influence. We therefore propose LatticeRank, a training-only framework that aligns candidate posteriors in physical space and allocates consistency supervision by voxel-spacing order: among endpoint-matched candidates, the auxiliary loss concentrates on the coarsest requested grid, while inference retains a single network forward with no candidate branch or ensemble. On MSD Task04, LatticeRank improves coarse-grid Extra Dice over mean consistency by 0.03436 across five seeds while meeting the prespecified reference-grid noninferiority criterion. A controlled ladder shows that grid exposure provides the largest recovery, posterior consistency adds a second gain, and spacing-ranked allocation yields the strongest direct-grid result; requested spacing thus provides a simple, model-independent ordering signal for direct-grid consistency learning.

cs.CV↗

arXiv2026

GAMF-Net: Grouped Availability-Aware Multimodal Fusion for Smartphone Skin Lesion Classification

Smartphone clinical images provide a low-cost data source for computer-aided skin lesion classification in resource-constrained settings, but images alone cannot fully capture patients' medical histories and lesion symptoms. To address the lack of structured organization among heterogeneous clinical variables, the ambiguity between observed and imputed values, and the risk that clinical information may interfere with visual evidence, we propose GAMF-Net, a grouped availability-aware multimodal fusion network. Ten clinical variables are divided into three semantic groups---demographics, history-related risk, and lesion presentation---and each group jointly encodes field values and field-level availability masks. The image branch produces the base classification logits, while each clinical group generates image-conditioned class-level residual evidence. Only available groups participate in availability-normalized equal-weight fusion; when all clinical information is unavailable, the model reduces exactly to the image-only pathway. We evaluate the method on PAD-UFES-20 under a patient-disjoint five-fold protocol with separate training, validation, and test partitions. GAMF-Net achieves an Accuracy of $74.17\pm2.85\%$, a balanced accuracy (BACC) of $70.11\pm2.11\%$, a Macro-F1 of $68.85\pm0.85\%$, and a Macro-AUROC of $93.42\pm1.20\%$, outperforming all image-only and multimodal baselines reimplemented under a unified protocol. Compared with the strongest baseline, MetaBlock, the corresponding improvements are 2.37, 2.59, 4.14, and 1.02 percentage points, respectively. Ablation results show that clinical information substantially improves class-balanced performance, constrained equal-weight fusion is more effective than dynamic fusion, and the lesion-presentation group provides the most informative clinical complement.

cs.CV↗

arXiv2026

AGGR-Mamba: Mamba-based Network with Attention-Guided Gated Residual Refinement for Undersampled MRI Reconstruction

Undersampled magnetic resonance imaging (MRI) reconstruction aims to recover high-quality images from partially acquired k-space data, thereby reducing scan time. However, missing frequency-domain measurements often lead to residual aliasing artifacts, blurred edges, and loss of local structural details. In this paper, we propose AGGR-Mamba, a lightweight output-stage residual refinement method built upon a Mamba backbone for undersampled brain MRI reconstruction. The Mamba backbone first produces an initial reconstruction, and the proposed refinement modules then correct the remaining local errors and high-frequency artifacts in a controlled residual manner. Specifically, an Attention Refine Head is introduced to recalibrate local anatomical structures, edge textures, and residual-error regions through channel and spatial attention. In addition, a GDFN-style gated residual refinement branch is designed to selectively preserve informative residual features while suppressing redundant or unstable responses. By combining attention-based recalibration with gated residual selection, AGGR-Mamba improves local structure recovery without substantially disturbing the backbone reconstruction. Experiments are conducted on the IXI brain MRI dataset with T1- and T2-weighted images under acceleration factors of R=4 and R=8. The results show that AGGR-Mamba achieves competitive PSNR and SSIM performance across different reconstruction settings, demonstrating the effectiveness of the proposed output-stage refinement strategy.

cs.CV↗