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9 results on '"Nath, Tanmay"'

4. Multi-animal pose estimation, identification and tracking with DeepLabCut

Author

Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Lauer, Jessy, Zhou, Mu, Ye, Shaokai, Menegas, William, Schneider, Steffen, Nath, Tanmay, Rahman, Mohammed Mostafizur, Di Santo, Valentina, Soberanes, Daniel, Feng, Guoping, Murthy, Venkatesh N, Lauder, George, Dulac, Catherine, Mathis, Mackenzie Weygandt, Mathis, Alexander, Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Lauer, Jessy, Zhou, Mu, Ye, Shaokai, Menegas, William, Schneider, Steffen, Nath, Tanmay, Rahman, Mohammed Mostafizur, Di Santo, Valentina, Soberanes, Daniel, Feng, Guoping, Murthy, Venkatesh N, Lauder, George, Dulac, Catherine, Mathis, Mackenzie Weygandt, and Mathis, Alexander

Abstract

AbstractEstimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking—features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal’s identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.

Published
2023

5. Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Author

Saxena, Sanjay, primary, Jena, Biswajit, additional, Gupta, Neha, additional, Das, Suchismita, additional, Sarmah, Deepaneeta, additional, Bhattacharya, Pallab, additional, Nath, Tanmay, additional, Paul, Sudip, additional, Fouda, Mostafa M., additional, Kalra, Manudeep, additional, Saba, Luca, additional, Pareek, Gyan, additional, and Suri, Jasjit S., additional

Published
2022
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6. Multi-animal pose estimation, identification and tracking with DeepLabCut

Author

Lauer, Jessy, Zhou, Mu, Ye, Shaokai, Menegas, William, Schneider, Steffen, Nath, Tanmay, Rahman, Mohammed Mostafizur, Di Santo, Valentina, Soberanes, Daniel, Feng, Guoping, Murthy, Venkatesh N., Lauder, George, Dulac, Catherine, Mathis, Mackenzie Weygandt, Mathis, Alexander, Lauer, Jessy, Zhou, Mu, Ye, Shaokai, Menegas, William, Schneider, Steffen, Nath, Tanmay, Rahman, Mohammed Mostafizur, Di Santo, Valentina, Soberanes, Daniel, Feng, Guoping, Murthy, Venkatesh N., Lauder, George, Dulac, Catherine, Mathis, Mackenzie Weygandt, and Mathis, Alexander

Abstract

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking—features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal’s identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.

Published
2022
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9. Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach.

Author

Ding N, Nath T, Damarla M, Gao L, and Hassoun PM

Subjects
Humans, Male, Female, Retrospective Studies, Middle Aged, Prognosis, Aged, Algorithms, Adult, Predictive Value of Tests, ROC Curve, Respiratory Distress Syndrome mortality, Respiratory Distress Syndrome diagnosis, Machine Learning
Abstract

Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this study was to evaluate the predictive values of dynamic clinical indices by developing machine-learning (ML) models for early and accurate clinical assessment of the disease prognosis of ARDS. We conducted a retrospective observational study by applying dynamic clinical data collected in the ARDSNet FACTT Trial (n = 1000) to ML-based algorithms for predicting mortality. In order to compare the significance of clinical features dynamically, we further applied the random forest (RF) model to nine selected clinical parameters acquired at baseline and day 3 independently. An RF model trained using clinical data collected at day 3 showed improved performance and prognostication efficacy (area under the curve [AUC]: 0.84, 95% CI: 0.78-0.89) compared to baseline with an AUC value of 0.72 (95% CI: 0.65-0.78). Mean airway pressure (MAP), bicarbonate, age, platelet count, albumin, heart rate, and glucose were the most significant clinical indicators associated with mortality at day 3. Thus, clinical features collected early (day 3) improved performance of integrative ML models with better prognostication for mortality. Among these, MAP represented the most important feature for ARDS patients' early risk stratification., (© 2024. The Author(s).)

Published
2024
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