Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Kalra, Shivam"'
Autor:
Adnan, Mohammed, Ba, Qinle, Shaikh, Nazim, Kalra, Shivam, Mukherjee, Satarupa, Lorsakul, Auranuch
Recent years have seen significant efforts to adopt Artificial Intelligence (AI) in healthcare for various use cases, from computer-aided diagnosis to ICU triage. However, the size of AI models has been rapidly growing due to scaling laws and the suc
Externí odkaz:
http://arxiv.org/abs/2404.08831
Chen et al. [Chen2022] recently published the article 'Fast and scalable search of whole-slide images via self-supervised deep learning' in Nature Biomedical Engineering. The authors call their method 'self-supervised image search for histology', sho
Externí odkaz:
http://arxiv.org/abs/2304.08297
Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task. The WSI embeddings obtained from current methods are in Euclidean space not ideal for efficient WSI retrieval. Furthermore, most of the
Externí odkaz:
http://arxiv.org/abs/2208.13653
Publikováno v:
Nature Communications 14, 2899 (2023)
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data wit
Externí odkaz:
http://arxiv.org/abs/2111.11343
Autor:
Kalra, Shivam, Adnan, Mohammed, Hemati, Sobhan, Dehkharghanian, Taher, Rahnamayan, Shahryar, Tizhoosh, Hamid
Deep learning methods such as convolutional neural networks (CNNs) are difficult to directly utilize to analyze whole slide images (WSIs) due to the large image dimensions. We overcome this limitation by proposing a novel two-stage approach. First, w
Externí odkaz:
http://arxiv.org/abs/2106.06623
The Kimia Path24 dataset has been introduced as a classification and retrieval dataset for digital pathology. Although it provides multi-class data, the color information has been neglected in the process of extracting patches. The staining informati
Externí odkaz:
http://arxiv.org/abs/2102.07611
Autor:
Riasatian, Abtin, Babaie, Morteza, Maleki, Danial, Kalra, Shivam, Valipour, Mojtaba, Hemati, Sobhan, Zaveri, Manit, Safarpoor, Amir, Shafiei, Sobhan, Afshari, Mehdi, Rasoolijaberi, Maral, Sikaroudi, Milad, Adnan, Mohd, Shah, Sultaan, Choi, Charles, Damaskinos, Savvas, Campbell, Clinton JV, Diamandis, Phedias, Pantanowitz, Liron, Kashani, Hany, Ghodsi, Ali, Tizhoosh, H. R.
Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the performance of image analysis can be improved through finetuning. As an ult
Externí odkaz:
http://arxiv.org/abs/2101.07903
Autor:
Ye, Jiarong1 (AUTHOR), Kalra, Shivam1 (AUTHOR) Shivam.Kalra@roche.com, Miri, Mohammad Saleh1 (AUTHOR)
Publikováno v:
Scientific Reports. 2/8/2024, Vol. 14 Issue 1, p1-10. 10p.
Autor:
Sriram, Aditya, Kalra, Shivam, Babaie, Morteza, Kieffer, Brady, Drobi, Waddah Al, Rahnamayan, Shahryar, Kashani, Hany, Tizhoosh, Hamid R.
In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images. The descriptor is based on the Radon transform wherein we app
Externí odkaz:
http://arxiv.org/abs/2008.03553
Autor:
Zaveri, Manit, Kalra, Shivam, Babaie, Morteza, Shah, Sultaan, Damskinos, Savvas, Kashani, Hany, Tizhoosh, H. R.
Recent advances in digital imaging has transformed computer vision and machine learning to new tools for analyzing pathology images. This trend could automate some of the tasks in the diagnostic pathology and elevate the pathologist workload. The fin
Externí odkaz:
http://arxiv.org/abs/2005.03748