Ensemble Learning with Sparse Hypercolumns

Multimodal MRI provides complementary information for brain tumor segmentation, but high accuracy alone does not reveal whether a model relies on clinically appropriate imaging evidence. Radiologists use different sequences for different findings, such as T1ce for enhancing tumor and FLAIR for peritumoral edema. Yet most 3D segmentation networks fuse MRI sequences through fixed connections, regardless of patient, region, or depth. We frame multimodal segmentation as grounded multimodal reasoning and introduce (i) a model agnostic probing framework combining per region modality ablation, Dominant Sensitivity Enhancement (DSE), and gate trajectory analysis to reveal sequence reliance, and (ii) evidence that adaptive Hyper-Connections (HC) induce clinically aligned modality reliance without modality supervision. Across nnU Net, SwinUNETR, and VT-UNet on BraTS 2021, most HC configurations increase reliance on clinically appropriate sequences. This interpretable pattern recurs across all three backbones, suggesting it arises from adaptive connections rather than a single architecture. HC also improves segmentation accuracy at negligible parameter cost. On validation, the best HC configuration increases mean Dice by 0.43 to 0.81 percentage points across the three backbones. On the held-out test set, it reduces mean HD95 by 1.17 to 1.78 mm (approximately 20 to 27 percent) for nnU-Net and VT UNet, adding at most 0.53 percent parameters. Our framework offers an inspectable measure of clinical grounding and extends naturally to multimodal medical foundation models.

Convolutional neural networks (CNNs) have become increasingly difficult to deploy in resource-constrained environments due to their large memory and computational requirements. Although low-rank compression methods can reduce this burden, most existing approaches compress spatial and channel redundancy independently and therefore do not fully exploit the localised structure within convolutional feature maps. This paper proposes a hierarchical spatio-channel low-rank compression framework for CNNs that exploits redundancy across spatial regions and channel activations. Unlike conventional methods, which apply a uniform decomposition across an entire layer, the proposed approach first partitions feature maps into spatial regions, then groups channels according to their co-activation patterns within each region, and finally applies rank-adaptive SVD to each resulting spatio-channel cluster. The method is evaluated on an AlexNet-based brain tumour MRI classification model and compared with Global SVD and Tucker decomposition under $3\times$ and $6\times$ compression budgets. Our method outperforms both baselines, reducing FLOPs from $8.21\,\mathrm{G}$ to $1.55\,\mathrm{G}$ ($81.1\%$ reduction), achieving a $1.38\times$ inference speed-up, and increasing classification accuracy from $87.76\%$ to $89.80\%$. The method also improves the macro $F_1$-score and performance on challenging classes such as meningioma. A hyper-parameter trade-off analysis demonstrates that the framework provides Pareto-optimal configurations, enabling control over the balance between compression and predictive performance. Moderate clustering with adaptive rank selection yields strong results. Bootstrap standard errors are reported for all classification metrics.

The rapid growth of image data has produced large-scale datasets, raising concerns about the time and memory costs of model training. Selecting representative training subsets, however, remains challenging: individual sample contributions are unclear, and model behavior varies across datasets and runs. We address these challenges with a framework that combines coreset selection with an ensemble aggregation over multiple runs. For coreset selection, we propose SCOre-Stratified Selection (SCOSS), which partitions the training data into intervals based on a chosen score and samples from each interval. The ensemble combines predictions from multiple runs, each performed on an independently sampled training subset. As baselines, we use moderate and random selection, each in original and class-balanced versions. We assess the framework with Simple Graph Convolution (SGC) and Support Vector Machine (SVM) classifiers under different sampling ratios. Experiments show that SCOSS is competitive with baselines, often the best choice for SGC, and enables favorable trade-offs between accuracy and efficiency. On the fine-grained dataset, SGC with SCOSS outperforms SVMs when using fewer labeled samples. The code and supplementary materials are publicly available at http://scoss.lucasvalem.com.