no code implementations • 1 Nov 2024 • Akhila Krishna, Nikhil Cherian Kurian, Abhijeet Patil, Amruta Parulekar, Amit Sethi
Accurate survival prediction is essential for personalized cancer treatment.
1 code implementation • 29 Sep 2024 • Abhijeet Patil, Harsh Diwakar, Jay Sawant, Nikhil Cherian Kurian, Subhash Yadav, Swapnil Rane, Tripti Bameta, Amit Sethi
HistoROI is trained using a novel human-in-the-loop and active learning paradigm that ensures variations in training data for labeling-efficient generalization.
no code implementations • 16 Jul 2023 • Akhila Krishna K, Ravi Kant Gupta, Nikhil Cherian Kurian, Pranav Jeevan, Amit Sethi
The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection.
no code implementations • 17 Mar 2023 • Nikhil Cherian Kurian, Varsha S, Abhijit PATIL, Shashikant Khade, Amit Sethi
The outlier score derived from the OOD detector is used to modulate sample selection for the subsequent semi-SL stage, ensuring that samples conforming to the distribution of the few labeled samples are more frequently exposed to the subsequent semi-SL framework.
no code implementations • 22 Feb 2023 • Pranav Jeevan, Nikhil Cherian Kurian, Amit Sethi
Convolution Neural Networks (CNNs) are widely used in medical image analysis, but their performance degrade when the magnification of testing images differ from the training images.
Ranked #3 on
Image Classification
on BreakHis
Breast Cancer Histology Image Classification
Deep Learning
+2
1 code implementation • 15 Sep 2022 • Tirupati Saketh Chandr, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Amit Sethi
The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade.
no code implementations • 26 Aug 2022 • Ravi Kant Gupta, Shivani Nandgaonkar, Nikhil Cherian Kurian, Swapnil Rane, Amit Sethi
With our pipeline, we achieved an average area under the curve (AUC) of 0. 964 for tumor detection, and 0. 942 for histological subtyping between adenocarcinoma and squamous cell carcinoma on the TCGA dataset.
no code implementations • 3 May 2022 • Nikhil Cherian Kurian, Amit Lohan, Gregory Verghese, Nimish Dharamshi, Swati Meena, Mengyuan Li, Fangfang Liu, Cheryl Gillet, Swapnil Rane, Anita Grigoriadis, Amit Sethi
Although the U-Net architecture has been extensively used for segmentation of medical images, we address two of its shortcomings in this work.
no code implementations • 5 Mar 2022 • Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Saqib Shamsi, Mohit Meena, Amit Sethi
We present WSSAMNet, a weakly supervised method for medical image registration.
no code implementations • 13 Dec 2021 • Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari, Michel Bilello, Benedikt Wiestler, Julian Schwarting, Evan Calabrese, Jeffrey Rudie, Syed Abidi, Mina Mousa, Javier Villanueva-Meyer, Brandon K. K. Fields, Florian Kofler, Russell Takeshi Shinohara, Juan Eugenio Iglesias, Tony C. W. Mok, Albert C. S. Chung, Marek Wodzinski, Artur Jurgas, Niccolo Marini, Manfredo Atzori, Henning Muller, Christoph Grobroehmer, Hanna Siebert, Lasse Hansen, Mattias P. Heinrich, Luca Canalini, Jan Klein, Annika Gerken, Stefan Heldmann, Alessa Hering, Horst K. Hahn, Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim, Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert, Javid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt, Kewei Yan, Yonghong Yan, Zhe Tang, Jianqiang Ma, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi, Nicholas J. Tustison, Brian B. Avants, Philip Cook, James C. Gee, Lin Tian, Hastings Greer, Marc Niethammer, Andrew Hoopes, Malte Hoffmann, Adrian V. Dalca, Stergios Christodoulidis, Theo Estiene, Maria Vakalopoulou, Nikos Paragios, Daniel S. Marcus, Christos Davatzikos, Aristeidis Sotiras, Bjoern Menze, Spyridon Bakas, Diana Waldmannstetter
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
no code implementations • 30 Nov 2020 • Abhijeet Patil, Mohd. Talha, Aniket Bhatia, Nikhil Cherian Kurian, Sammed Mangale, Sunil Patel, Amit Sethi
Performance of deep learning algorithms decreases drastically if the data distributions of the training and testing sets are different.