Zobrazeno 1 - 10
of 1 860
pro vyhledávání: '"GOWTHAM, P."'
Speech-based assessment of the schizophrenia spectrum has been widely researched over in the recent past. In this study, we develop a deep learning framework to estimate schizophrenia severity scores from speech using a feature fusion approach that f
Externí odkaz:
http://arxiv.org/abs/2411.06033
Autor:
Saha, Saheli, Banerjee, Debasmita, Ram, Rishi, Reddy, Gowtham, Guha, Debashree, Sarkar, Arnab, Dutta, Bapi, S, Moses ArunSingh, Chakraborty, Suman, Mallick, Indranil
Dose prediction is an area of ongoing research that facilitates radiotherapy planning. Most commercial models utilise imaging data and intense computing resources. This study aimed to predict the dose-volume of rectum and bladder from volumes of targ
Externí odkaz:
http://arxiv.org/abs/2411.05378
Autor:
Bukas, Christina, Subramanian, Harshavardhan, See, Fenja, Steinchen, Carina, Ezhov, Ivan, Boosarpu, Gowtham, Asgharpour, Sara, Burgstaller, Gerald, Lehmann, Mareike, Kofler, Florian, Piraud, Marie
High-throughput image analysis in the biomedical domain has gained significant attention in recent years, driving advancements in drug discovery, disease prediction, and personalized medicine. Organoids, specifically, are an active area of research,
Externí odkaz:
http://arxiv.org/abs/2410.14612
Autor:
Murugesan, Gowtham Krishnan, McCrumb, Diana, Soni, Rahul, Kumar, Jithendra, Nuernberg, Leonard, Pei, Linmin, Wagner, Ulrike, Granger, Sutton, Fedorov, Andrey Y., Moore, Stephen, Van Oss, Jeff
AI in Medical Imaging project aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by developing nnU-Net models and providing AI-assisted segmentations for cancer radiology images. We created high-quality, AI-annotated imagi
Externí odkaz:
http://arxiv.org/abs/2409.20342
Detecting and measuring confounding effects from data is a key challenge in causal inference. Existing methods frequently assume causal sufficiency, disregarding the presence of unobserved confounding variables. Causal sufficiency is both unrealistic
Externí odkaz:
http://arxiv.org/abs/2409.17840
Multimodal schizophrenia assessment systems have gained traction over the last few years. This work introduces a schizophrenia assessment system to discern between prominent symptom classes of schizophrenia and predict an overall schizophrenia severi
Externí odkaz:
http://arxiv.org/abs/2409.09733
Black hole perturbation theory on spherically symmetric backgrounds has been instrumental in establishing various aspects about the gravitational dynamics close to black holes, and continues to be an interesting avenue to confront current challenges
Externí odkaz:
http://arxiv.org/abs/2408.13557
Autor:
Vashishtha, Aniket, Kumar, Abhinav, Reddy, Abbavaram Gowtham, Balasubramanian, Vineeth N, Sharma, Amit
For text-based AI systems to interact in the real world, causal reasoning is an essential skill. Since interventional data is costly to generate, we study to what extent an agent can learn causal reasoning from passive data. Specifically, we consider
Externí odkaz:
http://arxiv.org/abs/2407.07612
Autor:
Premananth, Gowtham, Siriwardena, Yashish M., Resnik, Philip, Bansal, Sonia, Kelly, Deanna L., Espy-Wilson, Carol
This paper presents a novel multimodal framework to distinguish between different symptom classes of subjects in the schizophrenia spectrum and healthy controls using audio, video, and text modalities. We implemented Convolution Neural Network and Lo
Externí odkaz:
http://arxiv.org/abs/2406.09706
Autor:
Kuchibhotla, Hari Chandana, Kancheti, Sai Srinivas, Reddy, Abbavaram Gowtham, Balasubramanian, Vineeth N
Going beyond mere fine-tuning of vision-language models (VLMs), learnable prompt tuning has emerged as a promising, resource-efficient alternative. Despite their potential, effectively learning prompts faces the following challenges: (i) training in
Externí odkaz:
http://arxiv.org/abs/2405.07921