9 papers · 1 filter
Predicting Metastatic Risk from Primary Cancer Tissue Architecture via Distance-Aware Spatial Modeling
Sandesh Pokhrel, Hamid Manoochehri, Beatrice S Knudsen +1
Predicting distant metastasis from the digital H & E slides of the primary tumor is a critical yet challenging task in computational pathology. Multiple Instance Learning (MIL) app…
Weakly Supervised Contrastive Learning for Histopathology Patch Embeddings
Bodong Zhang, Xiwen Li, Hamid Manoochehri +4
Digital histopathology whole slide images (WSIs) provide gigapixel-scale high-resolution images that are highly useful for disease diagnosis. However, digital histopathology image…
DuoFormer: Leveraging Hierarchical Representations by Local and Global Attention Vision Transformer
Xiaoya Tang, Bodong Zhang, Man Minh Ho +2
Despite the widespread adoption of transformers in medical applications, the exploration of multi-scale learning through transformers remains limited, while hierarchical representa…
A Comparison of Object Detection and Phrase Grounding Models in Chest X-ray Abnormality Localization using Eye-tracking Data
Elham Ghelichkhan, Tolga Tasdizen
Chest diseases rank among the most prevalent and dangerous global health issues. Object detection and phrase grounding deep learning models interpret complex radiology data to assi…
WeakSupCon: Weakly Supervised Contrastive Learning for Encoder Pre-training
Bodong Zhang, Hamid Manoochehri, Xiwen Li +2
Weakly supervised multiple instance learning (MIL) is a challenging task given that only bag-level labels are provided, while each bag typically contains multiple instances. This t…
SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images
Hamid Manoochehri, Bodong Zhang, Beatrice S. Knudsen +1
Self-supervised learning has become a cornerstone in various areas, particularly histopathological image analysis. Image augmentation plays a crucial role in self-supervised learni…