Suyog Dutt Jain

Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani et autres

We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 …

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PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding

Jang Hyun Cho, Andrea Madotto, Effrosyni Mavroudi, Triantafyllos Afouras et autres

Vision-language models are integral to computer vision research, yet many high-performing models remain closed-source, obscuring their data, design and training recipe. The research community has responded by using distillation from black-box models to label training data, achieving strong benchmark results, at the …

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AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis

John H. Abel, Suyog Dutt Jain, Deepta Rajan, Harshith Padigela et autres

While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, …

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Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani et autres

We present Ego-Exo4D, a diverse, large-scale multi-modal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured ego-centric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 …

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Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors

Samreen Anjum, Suyog Dutt Jain, Danna Gurari

We propose a hybrid framework for consistently producing high-quality object tracks by combining an automated object tracker with little human input. The key idea is to tailor a module for each dataset to intelligently decide when an object tracker is failing and …

Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani et autres

We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 …

Deep-learning quantified cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer types

John F. Abel, Suyog Dutt Jain, Deepta Rajan, Harshith Padigela et autres

ABSTRACT While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus …

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Abstract P4-09-08: AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and predicts survival in breast cancer

John H. Abel, Christian Kirkup, Filip Kos, Ylaine Gerardin et autres

Abstract Background: Morphological features of cancer cell nuclei are routinely used to assess disease severity and prognosis, and cancer nuclear morphology has been linked to genomic alterations. Quantitative analyses of the nuclear features of cancer cells and other tumor-resident cell types, such …

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554 Artificial intelligence (AI)-powered immune phenotyping of advanced or metastatic urothelial carcinoma (aUC) clinical trial samples from hematoxylin and eosin (H&E)-stained whole slide images (WSI)

Jake Conway, Limin Yu, Yash Belhe, Suyog Dutt Jain et autres

Background CD8 immune phenotype status is associated with response to anti–PD-L1 therapy in aUC. To assess the tumor microenvironment in aUC, we developed machine learning (ML)–based models to identify cell types and tissue regions in digitized H&E-stained WSI from the JAVELIN Bladder …

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Abstract 464: AI-powered segmentation and analysis of nuclei morphology predicts genomic and clinical markers in multiple cancer types

John H. Abel, Suyog Dutt Jain, Deepta Rajan, Ken Leidal et autres

Abstract Morphological features of cancer cell nuclei are linked to gene expression signatures and genomic alterations. In addition, pathologists have leveraged nuclear morphology as diagnostic and prognostic markers. To enable the use of nuclear morphology in digital pathology, we developed a pan-tissue, …

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Abstract P5-02-02: Artificial intelligence powered predictive analysis of atypical ductal hyperplasia from digitized pathology images

Jennifer K. Kerner, Allison Cleary, Suyog Dutt Jain, Harsha Pokkalla et autres

Abstract Background: Approximately 15-25% of patients with atypical ductal hyperplasia (ADH) diagnosed on breast core needle biopsy (CNB) are upgraded to ductal carcinoma in situ (DCIS) or invasive carcinoma (IC) on surgical excision. The reproducible identification of patients with ADH on CNB …

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Click Carving: Interactive Object Segmentation in Images and Videos with Point Clicks

Suyog Dutt Jain, Kristen Grauman

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