2 citations · 5 across the 15 of their papers we have counts for
12 papers · 1 filter
What Makes High-Magnification Knowledge Transferable? A Study of Cross-Resolution Distillation in Whole-Slide Imaging
Zhiyuan Yang, Jiahao Cheng, Mahdi S. Hosseini
Cross-resolution knowledge distillation aims to improve low-magnification whole- slide analysis by transferring high-magnification representations, yet the conditions for useful tr…
Gated Spatial Redundancy Projection for Pathology Transformer Attentions
Zhiyuan Yang, Jiahao Cheng, Vincent Quoc-Huy Trinh +1
Transformer models are increasingly used for whole-slide image analysis in computational pathology. Yet, WSIs differ fundamentally from natural images: neighbouring patches often c…
Fisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning
Ghodsiyeh Rostami, Po-Han Chen, Mahdi S. Hosseini
Parameter-efficient fine-tuning often selects trainable parameters before adaptation using architectural heuristics, without accounting for their varying importance during training…
Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation
Zhiyuan Yang, Jiahao Cheng, Vincent Quoc-Huy Trinh +1
Generating clinically useful pathology reports for pathology cases from whole-slide images (WSIs) is challenging due to gigapixel resolution, long visual-token sequences, and the c…
MOOZY: A Patient-First Foundation Model for Computational Pathology
Yousef Kotp, Vincent Quoc-Huy Trinh, Christopher Pal +1
Computational pathology needs whole-slide image (WSI) foundation models that transfer across diverse clinical tasks, yet current approaches remain largely slide-centric, often depe…
MonoLoss: A Training Objective for Interpretable Monosemantic Representations
Ali Nasiri-Sarvi, Anh Tien Nguyen, Hassan Rivaz +2
Sparse autoencoders (SAEs) decompose polysemantic neural representations, where neurons respond to multiple unrelated concepts, into monosemantic features that capture single, inte…
SPARC: Concept-Aligned Sparse Autoencoders for Cross-Model and Cross-Modal Interpretability
Ali Nasiri-Sarvi, Hassan Rivaz, Mahdi S. Hosseini
Understanding how different AI models encode the same high-level concepts, such as objects or attributes, remains challenging because each model typically produces its own isolated…
LBMamba: Locally Bi-directional Mamba
Jingwei Zhang, Xi Han, Hong Qin +2
Mamba, a State Space Model (SSM) that accelerates training by recasting recurrence as a parallel scan, has recently emerged as a linearly-scaling alternative to self-attention. Bec…
Investigating Zero-Shot Diagnostic Pathology in Vision-Language Models with Efficient Prompt Design
Vasudev Sharma, Ahmed Alagha, Abdelhakim Khellaf +2
Vision-language models (VLMs) have gained significant attention in computational pathology due to their multimodal learning capabilities that enhance big-data analytics of giga-pix…
Ultrasound Image Generation using Latent Diffusion Models
Benoit Freiche, Anthony El-Khoury, Ali Nasiri-Sarvi +5
Diffusion models for image generation have been a subject of increasing interest due to their ability to generate diverse, high-quality images. Image generation has immense potenti…
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification
Jingwei Zhang, Anh Tien Nguyen, Xi Han +4
Efficiently modeling large 2D contexts is essential for various fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parall…
Vision Mamba for Classification of Breast Ultrasound Images
Ali Nasiri-Sarvi, Mahdi S. Hosseini, Hassan Rivaz
Mamba-based models, VMamba and Vim, are a recent family of vision encoders that offer promising performance improvements in many computer vision tasks. This paper compares Mamba-ba…