Mehran Hosseinzadeh

Gita Sarafraz, Armin Behnamnia, Mehran Hosseinzadeh et al.

Machine learning algorithms have revolutionized different fields, including natural language processing, computer vision, signal processing, and medical data processing. Despite the excellent capabilities of machine learning algorithms in various tasks and areas, the performance of these models mainly deteriorates when there is a shift in the test and training data distributions. This gap occurs due to the violation of the fundamental assumption that the training and test data are independent and identically distributed (i.i.d). In real-world scenarios where collecting data from all possible domains for training is costly and even impossible, the i.i.d assumption can hardly be satisfied. The problem is even more severe in the case of medical images and signals because it requires either expensive equipment or a meticulous experimentation setup to collect data, even for a single domain. Additionally, the decrease in performance may have severe consequences in the analysis of medical records. As a result of such problems, the ability to generalize and adapt under distribution shifts (domain generalization (DG) and domain adaptation (DA)) is essential for the analysis of medical data. This paper provides the first systematic review of DG and DA on functional brain signals to fill the gap of the absence of a comprehensive study in this era. We provide detailed explanations and categorizations of datasets, approaches, and architectures used in DG and DA on functional brain images. We further address the attention-worthy future tracks in this field.

Valay Bundele, Mehran Hosseinzadeh, Hendrik P. A. Lensch

Accurate surgical instrument segmentation in endoscopic videos is crucial for computer-assisted interventions, yet remains challenging due to frequent occlusions, rapid motion, specular artefacts, and long-term instrument re-entry. While SAM3 provides a powerful spatio-temporal framework for video object segmentation, its performance in surgical scenes is limited by indiscriminate memory updates, fixed memory capacity, and weak identity recovery after occlusions. We propose ReMeDI-SAM3, a training-free memory-enhanced extension of SAM3, that addresses these limitations through three components: (i) relevance-aware memory filtering with a dedicated occlusion-aware memory for storing pre-occlusion frames, (ii) a piecewise interpolation scheme that expands the effective memory capacity, and (iii) a feature-based re-identification module with temporal voting for reliable post-occlusion identity disambiguation. Together, these components mitigate error accumulation and enable reliable recovery after occlusions. Evaluations on EndoVis17 and EndoVis18 under a zero-shot setting show absolute mcIoU improvements of around 7% and 16%, respectively, over vanilla SAM3, outperforming even prior training-based approaches. Project page: https://valaybundele.github.io/remedi-sam3/.

Erik Großkopf, Valay Bundele, Mehran Hosseinzadeh et al.

Hematoxylin and Eosin (H&E) staining is the cornerstone of histopathology but lacks molecular specificity. While Immunohistochemistry (IHC) provides molecular insights, it is costly and complex, motivating H&E-to-IHC translation as a cost-effective alternative. Existing translation methods are mainly GAN-based, often struggling with training instability and limited structural fidelity, while diffusion-based approaches remain underexplored. We propose HistDiST, a Latent Diffusion Model (LDM) based framework for high-fidelity H&E-to-IHC translation. HistDiST introduces a dual-conditioning strategy, utilizing Phikon-extracted morphological embeddings alongside VAE-encoded H&E representations to ensure pathology-relevant context and structural consistency. To overcome brightness biases, we incorporate a rescaled noise schedule, v-prediction, and trailing timesteps, enforcing a zero-SNR condition at the final timestep. During inference, DDIM inversion preserves the morphological structure, while an eta-cosine noise schedule introduces controlled stochasticity, balancing structural consistency and molecular fidelity. Moreover, we propose Molecular Retrieval Accuracy (MRA), a novel pathology-aware metric leveraging GigaPath embeddings to assess molecular relevance. Extensive evaluations on MIST and BCI datasets demonstrate that HistDiST significantly outperforms existing methods, achieving a 28% improvement in MRA on the H&E-to-Ki67 translation task, highlighting its effectiveness in capturing true IHC semantics.

Valay Bundele, Mehran Hosseinzadeh, Hendrik Lensch

Synthetic datasets have enabled significant progress in point tracking by providing large-scale, densely annotated supervision. However, deploying these models in real-world domains remains challenging due to domain shift and lack of labeled data-issues that are especially severe in surgical videos, where scenes exhibit complex tissue deformation, occlusion, and lighting variation. While recent approaches adapt synthetic-trained trackers to natural videos using teacher ensembles or augmentation-heavy pseudo-labeling pipelines, their effectiveness in high-shift domains like surgery remains unexplored. This work presents SurgTracker, a semi-supervised framework for adapting synthetic-trained point trackers to surgical video using filtered self-distillation. Pseudo-labels are generated online by a fixed teacher-identical in architecture and initialization to the student-and are filtered using a cycle consistency constraint to discard temporally inconsistent trajectories. This simple yet effective design enforces geometric consistency and provides stable supervision throughout training, without the computational overhead of maintaining multiple teachers. Experiments on the STIR benchmark show that SurgTracker improves tracking performance using only 80 unlabeled videos, demonstrating its potential for robust adaptation in high-shift, data-scarce domains.