18 Papers
Neel Kanwal is an academic researcher from National University of Computer and Emerging Sciences. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 1, co-authored 2 publications.
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Papers
Quantifying the effect of color processing on blood and damaged tissue detection in Whole Slide Images
Neel Kanwal,Saul Fuster,Farbod Khoraminia,Tahlita C.M. Zuiverloon,Chunming Rong,Kjersti Engan +5 more
- 26 Jun 2022
TL;DR: Since blood and damaged tissue have subtle color differences, the impact of color processing methods on the binary classification performance of five well-known architectures is assessed and the effectiveness of transfer learning against training from scratch is measured.
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Invasive Cancerous Area Detection in Non-Muscle Invasive Bladder Cancer Whole Slide Images
Saul Fuster,Farbod Khoraminia,Umay Kiraz,Neel Kanwal,Vebjørn Kvikstad,Trygve Eftestøl,Tahlita C.M. Zuiverloon,Emiel A. M. Janssen,Kjersti Engan +8 more
- 26 Jun 2022
TL;DR: A multi-scale model is proposed that detects invasive cancerous areas patterns across the whole slide image and processes them to predict invasive patterns based on local and regional information for accurate T1 staging for non-muscle invasive bladder cancer patients.
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Vision Transformers for Small Histological Datasets Learned Through Knowledge Distillation
TL;DR: In this article , a student-teacher recipe is presented to improve the classification performance of the Vision Transformers for the air bubbles detection task. But, the proposed method is limited to the case of whole slide images (WSIs).
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Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images
Neel Kanwal,Miguel López-Pérez,Umay Kiraz,Tahlita C.M. Zuiverloon,Rafael Molina,Kjersti Engan +5 more
TL;DR: The proposed probabilistic model combines a CNN feature extractor and a sparse Gaussian Processes (GPs) classifier, which improves the performance of current state-of-the-art artifact detection DCNNs and provides uncertainty estimates.
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Automated Diagnosis of Prostate Cancer Using mpMRI Images: A Deep Learning Approach for Clinical Decision Support
Anil B. Gavade,Rajendra B Nerli,Neel Kanwal,P. A. Gavade,Shridhar Sunilkumar Pol,Syed Sajjad Hussain Rizvi +5 more
TL;DR: The proposed DL approach, with simpler architectures and training strategy using a single dataset, outperforms existing techniques in the literature and has a high potential to improve clinical assessment.
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