Search Results for author:
Found 28 papers, 13 papers with code
RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approaches
In this paper, we present a lightweight solution with a tunable temporal parameter, RT-GAN (Recurrent Temporal GAN), for adding temporal consistency to individual frame-based approaches that reduces training requirements by a factor of 5.
An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment
We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients.
RMSim: Controlled Respiratory Motion Simulation on Static Patient Scans
The predicted respiratory patterns, represented by time-varying displacement vector fields (DVFs) at different breathing phases, are modulated through auxiliary inputs of 1D breathing traces so that a larger amplitude in the trace results in more significant predicted deformation.
Stain-invariant self supervised learning for histopathology image analysis
Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets ranging from tumor classification (CAMELYON17) and subtyping (BRACS) to HER2 status classification and treatment response prediction.
Domain Knowledge Driven 3D Dose Prediction Using Moment-Based Loss Function
Model with (MAE + Moment) loss function outperformed the model with MAE loss by significantly improving the DVH-score (11%, p$<$0. 01) while having similar computational cost.
CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction
Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria.
CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy
Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections.
DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides
In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression.
FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos
Haustral folds are colon wall protrusions implicated for high polyp miss rate during optical colonoscopy procedures.
Deformation Driven Seq2Seq Longitudinal Tumor and Organs-at-Risk Prediction for Radiotherapy
Purpose: Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment.
Deep Learning 3D Dose Prediction for Conventional Lung IMRT Using Consistent/Unbiased Automated Plans
Moreover, any changes in the clinical criteria requires a new set of manually generated plans by planners to build a new prediction model.
Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation Using Physics-Based Data Augmentation
Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures.
Visualizing Missing Surfaces In Colonoscopy Videos using Shared Latent Space Representations
This shared latent space captures the geometric information while deferring the color, texture, and specular information creation to additional Gaussian noise input.
Generalizable Cone Beam CT Esophagus Segmentation Using Physics-Based Data Augmentation
Automated segmentation of esophagus is critical in image guided/adaptive radiotherapy of lung cancer to minimize radiation-induced toxicities such as acute esophagitis.
Multimarginal Wasserstein Barycenter for Stain Normalization and Augmentation
Variations in hematoxylin and eosin (H&E) stained images (due to clinical lab protocols, scanners, etc) directly impact the quality and accuracy of clinical diagnosis, and hence it is important to control for these variations for a reliable diagnosis.
Augmenting Colonoscopy using Extended and Directional CycleGAN for Lossy Image Translation
In this paper, we present a deep learning framework, Extended and Directional CycleGAN, for lossy unpaired image-to-image translation between OC and VC to augment OC video sequences with scale-consistent depth information from VC, and augment VC with patient-specific textures, color and specular highlights from OC (e. g, for realistic polyp synthesis).
Visualization Framework for Colonoscopy Videos
We present a visualization framework for annotating and comparing colonoscopy videos, where these annotations can then be used for semi-automatic report generation at the end of the procedure.
C2A: Crowd Consensus Analytics for Virtual Colonoscopy
In particular, C$^2$A is used to analyze and explore crowd responses on video segments, created from fly-throughs in the virtual colon.
Corresponding Supine and Prone Colon Visualization Using Eigenfunction Analysis and Fold Modeling
The use of Fiedler vectors and the segmented folds presents a precise way of visualizing corresponding regions across datasets and visual modalities.
FeatureLego: Volume Exploration Using Exhaustive Clustering of Super-Voxels
We then perform an exhaustive clustering of these super-voxels using a graph-based clustering method.
Radiative Transport Based Flame Volume Reconstruction from Videos
The approach includes an economical data capture technique using inexpensive CCD cameras.
LMap: Shape-Preserving Local Mappings for Biomedical Visualization
In this paper, we present a shape-preserving local mapping technique for resolving occlusions locally while preserving the overall geometric context.
Crowd-Assisted Polyp Annotation of Virtual Colonoscopy Videos
Virtual colonoscopy (VC) allows a radiologist to navigate through a 3D colon model reconstructed from a computed tomography scan of the abdomen, looking for polyps, the precursors of colon cancer.
Crowdsourcing Lung Nodules Detection and Annotation
More specifically, a complete workflow is introduced which can help maximize the sensitivity of lung nodule detection by utilizing the collective intelligence of the crowd.
Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening
The spiculation quantification measures was then applied to the radiomics framework for pathological malignancy prediction with reproducible semi-automatic segmentation of nodule.
Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response
Quantification of local metabolic tumor volume (MTV) chan-ges after Chemo-radiotherapy would allow accurate tumor response evaluation.
GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport
The glymphatic system (GS) is a transit passage that facilitates brain metabolic waste removal and its dysfunction has been associated with neurodegenerative diseases such as Alzheimer's disease.
Depth Reconstruction and Computer-Aided Polyp Detection in Optical Colonoscopy Video Frames
We present a computer-aided detection algorithm for polyps in optical colonoscopy images.
