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Rakshith Sathish
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2020 – today
- 2024
- [i4]Sparsh Pekhale, Rakshith Sathish, Sathisha Basavaraju, Divya Sharma:
SAModified: A Foundation Model-Based Zero-Shot Approach for Refining Noisy Land-Use Land-Cover Maps. CoRR abs/2412.12552 (2024) - 2023
- [j1]Aditya Raj, Rakshith Sathish
, Tandra Sarkar, Ramanathan Sethuraman
Designing Deep Neural High-Density Compression Engines for Radiology Images. Circuits Syst. Signal Process. 42(2): 643-682 (2023) - [c3]Ravi Kakaiya, Rakshith Sathish, Debdoot Sheet, Ramanathan Sethuraman
Exploiting Richness of Learned Compressed Representation of Images for Semantic Segmentation. ICME Workshops 2023: 482-485 - [i3]Ravi Kakaiya, Rakshith Sathish, Ramanathan Sethuraman, Debdoot Sheet:
Exploiting Richness of Learned Compressed Representation of Images for Semantic Segmentation. CoRR abs/2307.01524 (2023) - 2022
- [c2]Rakshith Sathish
Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks. DeCaF/FAIR@MICCAI 2022: 178-189 - [d2]Arunava Chakravarty, Tandra Sarkar, Rakshith Sathish, Ramanathan Sethuraman, Debdoot Sheet:
Re-curated Breast Imaging Subset DDSM Dataset (RBIS-DDSM). IEEE DataPort, 2022 - [i2]Rakshith Sathish, Swanand R. Khare, Debdoot Sheet:
Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks. CoRR abs/2209.15287 (2022) - 2020
- [c1]Rakshith Sathish
Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss. EMBC 2020: 1331-1334 - [d1]Debdoot Sheet, Arunava Chakravarty, Tandra Sarkar, Rakshith Sathish, Aditya Raj, Velmurugan Balasubramanian, Ronnie Rajan, Rachana Sathish, Nishant Chakravorty, Manjira Sinha, Manoj Sharma, Vikas Kumar, Rajiv Kumar, Abhishek Kumar, Atul Singhal, Gowtham Reddy:
Covid19action-radiology-CXR. IEEE DataPort, 2020 - [i1]Rakshith Sathish, Rachana Sathish, Ramanathan Sethuraman, Debdoot Sheet:
Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss. CoRR abs/2006.09308 (2020)
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