Sashank Makanaboyina

Sashank Makanaboyina

Accurate detection and segmentation of brain tumors in magnetic resonance imaging (MRI) are critical for effective diagnosis and treatment planning. Despite advances in convolutional neural networks (CNNs) such as U-Net, existing models often struggle with generalization, boundary precision, and limited data diversity. To address these challenges, we propose NNDM (NN\_UNet Diffusion Model)a hybrid framework that integrates the robust feature extraction of NN-UNet with the generative capabilities of diffusion probabilistic models. In our approach, the diffusion model progressively refines the segmentation masks generated by NN-UNet by learning the residual error distribution between predicted and ground-truth masks. This iterative denoising process enables the model to correct fine structural inconsistencies and enhance tumor boundary delineation. Experiments conducted on the BraTS 2021 datasets demonstrate that NNDM achieves superior performance compared to conventional U-Net and transformer-based baselines, yielding improvements in Dice coefficient and Hausdorff distance metrics. Moreover, the diffusion-guided refinement enhances robustness across modalities and tumor subregions. The proposed NNDM establishes a new direction for combining deterministic segmentation networks with stochastic diffusion models, advancing the state of the art in automated brain tumor analysis.

Sashank Makanaboyina

Incremental brain tumor segmentation is critical for models that must adapt to evolving clinical datasets without retraining on all prior data. However, catastrophic forgetting, where models lose previously acquired knowledge, remains a major obstacle. Recent incremental learning frameworks with knowledge distillation partially mitigate forgetting but rely heavily on generative replay or auxiliary storage. Meanwhile, diffusion models have proven effective for refining tumor segmentations, but have not been explored in incremental learning contexts. We propose Synthetic Error Replay Diffusion (SER-Diff), the first framework that unifies diffusion-based refinement with incremental learning. SER-Diff leverages a frozen teacher diffusion model to generate synthetic error maps from past tasks, which are replayed during training on new tasks. A dual-loss formulation combining Dice loss for new data and knowledge distillation loss for replayed errors ensures both adaptability and retention. Experiments on BraTS2020, BraTS2021, and BraTS2023 demonstrate that SER-Diff consistently outperforms prior methods. It achieves the highest Dice scores of 95.8\%, 94.9\%, and 94.6\%, along with the lowest HD95 values of 4.4 mm, 4.7 mm, and 4.9 mm, respectively. These results indicate that SER-Diff not only mitigates catastrophic forgetting but also delivers more accurate and anatomically coherent segmentations across evolving datasets.