2 citations · 4 across the 11 of their papers we have counts for
12 papers
BigCell: Generating Gigapixel Whole-Slide Images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li +10
Whole-slide images (WSIs) are gigapixel-scale digital scans of histopathology tissue and serve as the primary basis for cancer diagnosis. Existing histopathology generative models…
Pathologist Attention-Aligned Report Generation for Prostate Histopathology
Ruoyu Xue, Suryakant Singh, Souradeep Chakraborty +12
The allocation of visual attention by pathologists during cancer diagnosis is a highly selective process that critically shapes the information extracted from whole-slide images (W…
PathReportEval: A Systematic Benchmark for Pathology Report Generation
Suryakant Singh, Sejuti Majumder, Beatrice Knudsen +2
Pathology report generation from whole-slide images (WSIs) is a rapidly growing multimodal learning problem, yet progress is difficult to measure because existing studies use heter…
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…
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…
DuoFormer: Leveraging Hierarchical Visual Representations by Local and Global Attention
Xiaoya Tang, Bodong Zhang, Beatrice S. Knudsen +1
We here propose a novel hierarchical transformer model that adeptly integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the advanced represe…
DISC: Latent Diffusion Models with Self-Distillation from Separated Conditions for Prostate Cancer Grading
Man M. Ho, Elham Ghelichkhan, Yosep Chong +3
Latent Diffusion Models (LDMs) can generate high-fidelity images from noise, offering a promising approach for augmenting histopathology images for training cancer grading models.…
F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation
Man M. Ho, Shikha Dubey, Yosep Chong +2
The Frozen Section (FS) technique is a rapid and efficient method, taking only 15-30 minutes to prepare slides for pathologists' evaluation during surgery, enabling immediate decis…
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…