2 citations · 3 across the 8 of their papers we have counts for
10 papers
Quantifying Volumetric Risk: Class-Aware Asymmetric Weighted Conformal Prediction for 3D Medical Image Segmentation
Shadi Alijani, Fereshteh Aghaee Meibodi, Homayoun Najjaran
Reliable volumetric segmentation is critical for clinical diagnostics, yet foundation models such as MedSAM remain deterministic and lack calibrated uncertainty under distribution…
Template-Search Domain Adaptation via Multi-Stage Feature Alignment for Cross-Modal Object Tracking
Fereshteh Aghaee Meibodi, Amir Mehdi Soufi Enayati, Shadi Alijani +1
Visual object tracking typically assumes that the initial template and subsequent search frames share the same sensing modality. In practice, sensor availability or operation may c…
Sub-Region-Aware Modality Fusion and Adaptive Prompting for Multi-Modal Brain Tumor Segmentation
Shadi Alijani, Fereshteh Aghaee Meibodi, Homayoun Najjaran
The successful adaptation of foundation models to multi-modal medical imaging is a critical yet unresolved challenge. Existing models often struggle to effectively fuse information…
A Deep Dive into Generic Object Tracking: A Survey
Fereshteh Aghaee Meibodi, Shadi Alijani, Homayoun Najjaran
Generic object tracking remains an important yet challenging task in computer vision due to complex spatio-temporal dynamics, especially in the presence of occlusions, similar dist…
WQLCP: Weighted Adaptive Conformal Prediction for Robust Uncertainty Quantification Under Distribution Shifts
Shadi Alijani, Homayoun Najjaran
Conformal prediction (CP) provides a framework for constructing prediction sets with guaranteed coverage, assuming exchangeable data. However, real-world scenarios often involve di…
Domain Adaptive Skin Lesion Classification via Conformal Ensemble of Vision Transformers
Mehran Zoravar, Shadi Alijani, Homayoun Najjaran
Exploring the trustworthiness of deep learning models is crucial, especially in critical domains such as medical imaging decision support systems. Conformal prediction has emerged…
Sliced Wasserstein Discrepancy in Disentangling Representation and Adaptation Networks for Unsupervised Domain Adaptation
Joel Sol, Shadi Alijani, Homayoun Najjaran
This paper introduces DRANet-SWD as a novel complete pipeline for disentangling content and style representations of images for unsupervised domain adaptation (UDA). The approach b…
Sim-to-Real Domain Adaptation for Deformation Classification
Joel Sol, Jamil Fayyad, Shadi Alijani +1
Deformation detection is vital for enabling accurate assessment and prediction of structural changes in materials, ensuring timely and effective interventions to maintain safety an…
Vision transformers in domain adaptation and domain generalization: a study of robustness
Shadi Alijani, Jamil Fayyad, Homayoun Najjaran
Deep learning models are often evaluated in scenarios where the data distribution is different from those used in the training and validation phases. The discrepancy presents a cha…
Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification
Jamil Fayyad, Shadi Alijani, Homayoun Najjaran
Background and objective: Uncertainty quantification is a pivotal field that contributes to realizing reliable and robust systems. It becomes instrumental in fortifying safe decisi…