645 citations · 1.5k across the 36 of their papers we have counts for
26 papers · 1 filter
A Foundational Multimodal Vision Language AI Assistant for Human Pathology
Ming Y. Lu, Bowen Chen, Drew F. K. Williamson +8
The field of computational pathology has witnessed remarkable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders.…
A General-Purpose Self-Supervised Model for Computational Pathology
Richard J. Chen, Tong Ding, Ming Y. Lu +17
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole…
Towards a Visual-Language Foundation Model for Computational Pathology
Ming Y. Lu, Bowen Chen, Drew F. K. Williamson +10
The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of di…
Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
Ming Y. Lu, Bowen Chen, Andrew Zhang +6
Contrastive visual language pretraining has emerged as a powerful method for either training new language-aware image encoders or augmenting existing pretrained models with zero-sh…
Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction
Guillaume Jaume, Anurag Vaidya, Richard Chen +3
Integrating whole-slide images (WSIs) and bulk transcriptomics for predicting patient survival can improve our understanding of patient prognosis. However, this multimodal task is…
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images
Imaad Zaffar, Guillaume Jaume, Nasir Rajpoot +1
Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelin…
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
Richard J. Chen, Chengkuan Chen, Yicong Li +4
Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for lo…
Algorithm Fairness in AI for Medicine and Healthcare
Richard J. Chen, Tiffany Y. Chen, Jana Lipkova +5
In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. Rece…
Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning
Richard J. Chen, Ming Y. Lu, Drew F. K. Williamson +8
The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. Howe…
Fast and Scalable Image Search For Histology
Chengkuan Chen, Ming Y. Lu, Drew F. K. Williamson +3
The expanding adoption of digital pathology has enabled the curation of large repositories of histology whole slide images (WSIs), which contain a wealth of information. Similar pa…
VR-Caps: A Virtual Environment for Capsule Endoscopy
Kagan Incetan, Ibrahim Omer Celik, Abdulhamid Obeid +8
Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate…
EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner
Kutsev Bengisu Ozyoruk, Guliz Irem Gokceler, Gulfize Coskun +12
Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not sup…
EndoL2H: Deep Super-Resolution for Capsule Endoscopy
Yasin Almalioglu, Kutsev Bengisu Ozyoruk, Abdulkadir Gokce +8
Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both…
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis
Richard J. Chen, Ming Y. Lu, Jingwen Wang +4
Cancer diagnosis, prognosis, and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data. However, mo…
Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks
Jingwen Wang, Richard J. Chen, Ming Y. Lu +2
Histology-based grade classification is clinically important for many cancer types in stratifying patients distinct treatment groups. In prostate cancer, the Gleason score is a gra…
Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding
Ming Y. Lu, Richard J. Chen, Jingwen Wang +2
Convolutional neural networks can be trained to perform histology slide classification using weak annotations with multiple instance learning (MIL). However, given the paucity of l…
Structured Prediction using cGANs with Fusion Discriminator
Faisal Mahmood, Wenhao Xu, Nicholas J. Durr +2
We propose the fusion discriminator, a single unified framework for incorporating conditional information into a generative adversarial network (GAN) for a variety of distinct stru…
Multimodal Densenet
Faisal Mahmood, Ziyun Yang, Thomas Ashley +1
Humans make accurate decisions by interpreting complex data from multiple sources. Medical diagnostics, in particular, often hinge on human interpretation of multi-modal informatio…
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images
Faisal Mahmood, Daniel Borders, Richard Chen +4
Nuclei segmentation is a fundamental task that is critical for various computational pathology applications including nuclei morphology analysis, cell type classification, and canc…
DeepLSR: a deep learning approach for laser speckle reduction
Taylor L. Bobrow, Faisal Mahmood, Miguel Inserni +1
Speckle artifacts degrade image quality in virtually all modalities that utilize coherent energy, including optical coherence tomography, reflectance confocal microscopy, ultrasoun…
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images
Faisal Mahmood, Richard Chen, Sandra Sudarsky +2
Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data…
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training
Faisal Mahmood, Richard Chen, Nicholas J. Durr
To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to acquire because labeled medic…
Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy
Faisal Mahmood, Nicholas J. Durr
Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading cause in the United States. The risk of colorectal cancer can be mitigated by the id…
Adaptive Graph-based Total Variation for Tomographic Reconstructions
Faisal Mahmood, Nauman Shahid, Ulf Skoglund +1
Sparsity exploiting image reconstruction (SER) methods have been extensively used with Total Variation (TV) regularization for tomographic reconstructions. Local TV methods fail to…