medical image analysis developers

Abstract: Preoperative prediction of postoperative best-corrected visual acuity (BCVA) can support prognosis assessment and individualized decision-making in cataract surgery. Multimodal learning can integrate preoperative optical coherence tomography (OCT) images, scanning laser ophthalmoscopy (SLO) images, and clinical text to characterize patient status. However, SLO images are often missing in real-world clinical data due to acquisition or data-management issues, resulting in incomplete multimodal inputs. To address this problem, this paper proposes a conditional diffusion-based method for SLO proxy representation recovery and fusion prediction. Instead of reconstructing raw SLO images, the proposed method formulates SLO missingness as proxy representation recovery in the latent feature space. Specifically, OCT image features and clinical text features are first fused to construct an OCT-text conditional representation, which guides the reverse denoising process of the diffusion model to estimate sample-specific SLO proxy representations. The recovered SLO proxy representation is then used as a substitute for the unavailable SLO modality and incorporated into the downstream multimodal fusion prediction network for postoperative BCVA prediction. Experiments were conducted on 2,141 preoperative multimodal cataract samples, with an SLO missing rate of 58.80%. The proposed method achieved an accuracy of 0.9714 under the criterion of an absolute prediction error within 0.1 LogMAR and a mean absolute error of 0.0423, outperforming representative postoperative visual acuity prediction and missing-modality learning methods. These results indicate that representation-level proxy recovery can alleviate the impact of SLO modality missingness on postoperative BCVA prediction.

Abstract: Quantifying glomerular inflammation in kidney-transplant biopsies is traditionally performed on single whole-slide sections using the Banff "most-severe section" rule, despite the inherently three-dimensional nature of lesion distribution. This 2D based assessment might lead to instability when inflammation varies across slices. This paper reports a feasibility study examining the technical feasibility and clinical relevance of extending Banff scoring into three dimensions. To this end, we propose a 3D Banff lesion-scoring framework that aligns serial sections, reconstructs glomerular identities across depth, and integrates inflammatory-cell counts in 3D under three scoring strategies: Compatible, Volumetric, and Deduplicated Volumetric. In the experiments, glomerulitis (g-scores) was used as an example Banff metric and applied to multi-section renal allograft biopsies. Our findings indicate that 3D Banff g-scores are more consistent across sections and, under the semi-automatic setting, the Volumetric strategy correlates more strongly with clinical biomarkers than traditional 2D scores. These results show that 3D volumetric quantification offers promising added value, underscoring the potential benefit of 3D-aware Banff scoring for kidney transplant pathology.

Abstract: Machine learning on resting-state functional magnetic resonance imaging (rs-fMRI) brain networks is promising for diagnosing Autism Spectrum Disorder (ASD), but existing methods are prone to overfitting on limited training data, and their post-hoc statistical attribution offers limited clinical interpretability. We propose CFBrainAug, a counterfactual brain network learning framework that unifies data augmentation and counterfactual explanation by learning label-altered brain networks. Unlike conventional label-preserving augmentation, CFBrainAug first retrieves a label-opposite yet topologically similar candidate and then refines it under a module-number constraint. Jointly training on factual and counterfactual networks enriches the training distribution around the decision boundary, enabling a lightweight feature-selection-based SVM to perform competitively with recent graph deep learning methods. Meanwhile, the counterfactuals provide intrinsic explanations by identifying the connectivity differences between each subject and its counterfactual. Experiments on the ABIDE I dataset show that CFBrainAug reaches an accuracy of 74.1% on the NYU site, suggesting that counterfactual augmentation can complement more complex models in small-sample brain network analysis.