Search Results for author: Rajarsi Gupta

Found 16 papers, 6 papers with code

Halcyon -- A Pathology Imaging and Feature analysis and Management System

1 code implementation7 Apr 2023 Erich Bremer, Tammy DiPrima, Joseph Balsamo, Jonas Almeida, Rajarsi Gupta, Joel Saltz

Halcyon is a new pathology imaging analysis and feature management system based on W3C linked-data open standards and is designed to scale to support the needs for the voluminous production of features from deep-learning feature pipelines.


ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis

no code implementations3 Apr 2023 Xuan Xu, Saarthak Kapse, Rajarsi Gupta, Prateek Prasanna

This marks the first time that ViT has been introduced to diffusion autoencoders in computational pathology, allowing the model to better capture the complex and intricate details of histopathology images.

Denoising Image Generation

Gigapixel Whole-Slide Images Classification using Locally Supervised Learning

1 code implementation17 Jul 2022 Jingwei Zhang, Xin Zhang, Ke Ma, Rajarsi Gupta, Joel Saltz, Maria Vakalopoulou, Dimitris Samaras

Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses.

Classification Multiple Instance Learning +1

AI and Pathology: Steering Treatment and Predicting Outcomes

no code implementations15 Jun 2022 Rajarsi Gupta, Jakub Kaczmarzyk, Soma Kobayashi, Tahsin Kurc, Joel Saltz

The combination of data analysis methods, increasing computing capacity, and improved sensors enable quantitative granular, multi-scale, cell-based analyses.

Visual attention analysis of pathologists examining whole slide images of Prostate cancer

no code implementations17 Feb 2022 Souradeep Chakraborty, Ke Ma, Rajarsi Gupta, Beatrice Knudsen, Gregory J. Zelinsky, Joel H. Saltz, Dimitris Samaras

To quantify the relationship between a pathologist's attention and evidence for cancer in the WSI, we obtained tumor annotations from a genitourinary specialist.

Navigate whole slide images

Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types

1 code implementation18 Feb 2020 Le Hou, Rajarsi Gupta, John S. Van Arnam, Yuwei Zhang, Kaustubh Sivalenka, Dimitris Samaras, Tahsin M. Kurc, Joel H. Saltz

To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process.

Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types

no code implementations9 Jul 2019 Shahira Abousamra, Le Hou, Rajarsi Gupta, Chao Chen, Dimitris Samaras, Tahsin Kurc, Rebecca Batiste, Tianhao Zhao, Shroyer Kenneth, Joel Saltz

This allows for a much larger training set, that reflects visual variability across multiple cancer types and thus training of a single network which can be automatically applied to each cancer type without human adjustment.

General Classification

Label Super Resolution with Inter-Instance Loss

no code implementations9 Apr 2019 Maozheng Zhao, Le Hou, Han Le, Dimitris Samaras, Nebojsa Jojic, Danielle Fassler, Tahsin Kurc, Rajarsi Gupta, Kolya Malkin, Shroyer Kenneth, Joel Saltz

On the other hand, collecting low resolution labels (labels for a block of pixels) for these high resolution images is much more cost efficient.

Semantic Segmentation Super-Resolution

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