8 papers
Predicting Metastatic Risk from Primary Tissue Architecture via Distance-Aware Spatial Modeling
Sandesh Pokhrel, Hamid Manoochehri, Bodong Zhang +2
Predicting the risk of distant metastasis from primary tumor tissue histology is a critical yet challenging task in computational pathology. Multiple Instance Learning (MIL) approa…
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…
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…
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…
CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification
Bodong Zhang, Hamid Manoochehri, Man Minh Ho +5
Histopathological image classification is an important task in medical image analysis. Recent approaches generally rely on weakly supervised learning due to the ease of acquiring c…