Výsledky vyhledávání - "Sethuraman, Ramanathan"

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Exploiting Richness of Learned Compressed Representation of Images for Semantic Segmentation

Autor: Kakaiya, Ravi, Sathish, Rakshith, Sethuraman, Ramanathan, Sheet, Debdoot

Autonomous vehicles and Advanced Driving Assistance Systems (ADAS) have the potential to radically change the way we travel. Many such vehicles currently rely on segmentation and object detection algorithms to detect and track objects around its surr

Externí odkaz: http://arxiv.org/abs/2307.01524

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Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss

Autor: Sathish, Rakshith, Sathish, Rachana, Sethuraman, Ramanathan, Sheet, Debdoot

Lung cancer is the most common form of cancer found worldwide with a high mortality rate. Early detection of pulmonary nodules by screening with a low-dose computed tomography (CT) scan is crucial for its effective clinical management. Nodules which

Externí odkaz: http://arxiv.org/abs/2006.09308

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A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms

Autor: K, Sarath Chandra, Chakravarty, Arunava, Ghosh, Nirmalya, Sarkar, Tandra, Sethuraman, Ramanathan, Sheet, Debdoot

Mammograms are commonly employed in the large scale screening of breast cancer which is primarily characterized by the presence of malignant masses. However, automated image-level detection of malignancy is a challenging task given the small size of

Externí odkaz: http://arxiv.org/abs/2004.11726

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Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening

Autor: Chakravarty, Arunava, Sarkar, Tandra, Ghosh, Nirmalya, Sethuraman, Ramanathan, Sheet, Debdoot

Chest radiographs are primarily employed for the screening of cardio, thoracic and pulmonary conditions. Machine learning based automated solutions are being developed to reduce the burden of routine screening on Radiologists, allowing them to focus

Externí odkaz: http://arxiv.org/abs/2004.11721

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A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs

Autor: Mitra, Arka, Chakravarty, Arunava, Ghosh, Nirmalya, Sarkar, Tandra, Sethuraman, Ramanathan, Sheet, Debdoot

Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditions. Being undertaken at primary healthcare centers, they require the presence of an on-premise reporting Radiologist, which is a challenge in low and

Externí odkaz: http://arxiv.org/abs/2004.11693

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Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms

Autor: Kar, Aupendu, Karri, Sri Phani Krishna, Ghosh, Nirmalya, Sethuraman, Ramanathan, Sheet, Debdoot

Early works on medical image compression date to the 1980's with the impetus on deployment of teleradiology systems for high-resolution digital X-ray detectors. Commercially deployed systems during the period could compress 4,096 x 4,096 sized images

Externí odkaz: http://arxiv.org/abs/1805.06909

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Akademický článek

MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication.

Autor: Kumar, Tarun^1,2,3 (AUTHOR), Sethuraman, Ramanathan⁴ (AUTHOR), Mitra, Sanga¹ (AUTHOR), Ravindran, Balaraman^1,2,3 (AUTHOR), Narayanan, Manikandan^1,2,3,5 (AUTHOR) nmanik@cse.iitm.ac.in

Publikováno v: PLoS Computational Biology. 4/24/2023, Vol. 19 Issue 4, p1-33. 33p. 2 Diagrams, 1 Chart, 4 Graphs.