16 citations · 58 across the 18 of their papers we have counts for
20 papers
Topo-R1: Detecting Topological Anomalies via Vision-Language Models
Meilong Xu, Qingqiao Hu, Xiaoling Hu +6
Topology is critical in tubular structures such as blood vessels, nerve fibers, and road networks, where connectivity and loop structure govern downstream functional analysis. Visi…
Act Like a Pathologist: Tissue-Aware Whole Slide Image Reasoning
Wentao Huang, Weimin Lyu, Peiliang Lou +8
Computational pathology has advanced rapidly in recent years, driven by domain-specific image encoders and growing interest in using vision-language models to answer natural-langua…
MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation
Meilong Xu, Xiaoling Hu, Shahira Abousamra +2
In semi-supervised segmentation, capturing meaningful semantic structures from unlabeled data is essential. This is particularly challenging in histopathology image analysis, where…
TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model
Meilong Xu, Saumya Gupta, Xiaoling Hu +5
Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology. The synthetic generation of cel…
RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency
Wentao Huang, Meilong Xu, Xiaoling Hu +9
Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization.…
Hard Negative Sample Mining for Whole Slide Image Classification
Wentao Huang, Xiaoling Hu, Shahira Abousamra +2
Weakly supervised whole slide image (WSI) classification is challenging due to the lack of patch-level labels and high computational costs. State-of-the-art methods use self-superv…
Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images
Mahmudul Hasan, Xiaoling Hu, Shahira Abousamra +3
Despite the strong prediction power of deep learning models, their interpretability remains an important concern. Disentanglement models increase interpretability by decomposing th…
Spatial Diffusion for Cell Layout Generation
Chen Li, Xiaoling Hu, Shahira Abousamra +2
Generative models, such as GANs and diffusion models, have been used to augment training sets and boost performances in different tasks. We focus on generative models for cell dete…
Semi-supervised Segmentation of Histopathology Images with Noise-Aware Topological Consistency
Meilong Xu, Xiaoling Hu, Saumya Gupta +2
In digital pathology, segmenting densely distributed objects like glands and nuclei is crucial for downstream analysis. Since detailed pixel-wise annotations are very time-consumin…
Calibrating Uncertainty for Semi-Supervised Crowd Counting
Chen Li, Xiaoling Hu, Shahira Abousamra +1
Semi-supervised crowd counting is an important yet challenging task. A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set…
Topology-Guided Multi-Class Cell Context Generation for Digital Pathology
Shahira Abousamra, Rajarsi Gupta, Tahsin Kurc +3
In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging…
Evaluating histopathology transfer learning with ChampKit
Jakub R. Kaczmarzyk, Tahsin M. Kurc, Shahira Abousamra +3
Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopat…
A Novel Framework for Characterization of Tumor-Immune Spatial Relationships in Tumor Microenvironment
Mahmudul Hasan, Jakub R. Kaczmarzyk, David Paredes +9
Understanding the impact of tumor biology on the composition of nearby cells often requires characterizing the impact of biologically distinct tumor regions. Biomarkers have been d…
Federated Learning for the Classification of Tumor Infiltrating Lymphocytes
Ujjwal Baid, Sarthak Pati, Tahsin M. Kurc +6
We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as…
Multi-Class Cell Detection Using Spatial Context Representation
Shahira Abousamra, David Belinsky, John Van Arnam +7
In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks. Classifying cells into subtypes, such as tumor cells,…
Localization in the Crowd with Topological Constraints
Shahira Abousamra, Minh Hoai, Dimitris Samaras +1
We address the problem of crowd localization, i.e., the prediction of dots corresponding to people in a crowded scene. Due to various challenges, a localization method is prone to…
Exascale Deep Learning to Accelerate Cancer Research
Robert M. Patton, J. Travis Johnston, Steven R. Young +9
Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural n…
Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types
Shahira Abousamra, Le Hou, Rajarsi Gupta +7
Deep learning classifiers for characterization of whole slide tissue morphology require large volumes of annotated data to learn variations across different tissue and cancer types…
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
Han Le, Rajarsi Gupta, Le Hou +12
Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and eva…
Label Super Resolution with Inter-Instance Loss
Maozheng Zhao, Le Hou, Han Le +8
For the task of semantic segmentation, high-resolution (pixel-level) ground truth is very expensive to collect, especially for high resolution images such as gigapixel pathology im…