118 citations · 323 across the 22 of their papers we have counts for
6 papers · 1 filter
Efficient Knowledge Distillation of SAM for Medical Image Segmentation
Kunal Dasharath Patil, Gowthamaan Palani, Ganapathy Krishnamurthi
The Segment Anything Model (SAM) has set a new standard in interactive image segmentation, offering robust performance across various tasks. However, its significant computational…
PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Net
Akansh Maurya, Kunal Dashrath Patil, Rohan Padhy +2
In this work, we present our proposed method to segment the pulmonary arteries from the CT scans using Swin UNETR and U-Net-based deep neural network architecture. Six models, thre…
Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture
Vikas Kumar Anand, Sanjeev Grampurohit, Pranav Aurangabadkar +4
We utilize 3-D fully convolutional neural networks (CNN) to segment gliomas and its constituents from multimodal Magnetic Resonance Images (MRI). The architecture uses dense connec…
Structurally aware bidirectional unpaired image to image translation between CT and MR
Vismay Agrawal, Avinash Kori, Vikas Kumar Anand +1
Magnetic Resonance (MR) Imaging and Computed Tomography (CT) are the primary diagnostic imaging modalities quite frequently used for surgical planning and analysis. A general probl…
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis
Mahendra Khened, Avinash Kori, Haran Rajkumar +2
Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Given the large size of these images and the increase in the number of potential c…
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis
Parth Natekar, Avinash Kori, Ganapathy Krishnamurthi
The accurate automatic segmentation of gliomas and its intra-tumoral structures is important not only for treatment planning but also for follow-up evaluations. Several methods bas…