3 citations · 3 across the 4 of their papers we have counts for
4 papers
Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation
Mohamad Koohi-Moghadam, Mohammad-Ali Nikouei Mahani, Rex K. H. Au-Yeung +6
Expert-annotated training data remains the critical bottleneck for AI in histopathology, particularly for rare pathologies where even dozens of cases may be unavailable. While data…
Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows
Dariush Lotfi, Mohammad-Ali Nikouei Mahani, Mohamad Koohi-Moghadam +1
In AI-driven medical imaging, the failure to detect out-of-distribution (OOD) data poses a severe risk to clinical reliability, potentially leading to critical diagnostic errors. C…
AutoPET III Challenge: PET/CT Semantic Segmentation
Reza Safdari, Mohammad Koohi-Moghaddam, Kyongtae Tyler Bae
In this study, we implemented a two-stage deep learning-based approach to segment lesions in PET/CT images for the AutoPET III challenge. The first stage utilized a DynUNet model f…
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis
Morteza Homayounfar, Mohamad Koohi-Moghadam, Reza Rawassizadeh +1
As deep neural networks include a high number of parameters and operations, it can be a challenge to implement these models on devices with limited computational resources. Despite…