132 citations · 332 across the 30 of their papers we have counts for
9 papers · 1 filter
RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection
Soumen Basu, Mayank Gupta, Pratyaksha Rana +2
We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis. Our architecture generates a global attention for region of in…
Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention
Ashutosh Agarwal, Chetan Arora
Monocular Depth Estimation (MDE) aims to predict pixel-wise depth given a single RGB image. For both, the convolutional as well as the recent attention-based models, encoder-decode…
Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain Shift
Sharat Agarwal, Saket Anand, Chetan Arora
In Active Domain Adaptation (ADA), one uses Active Learning (AL) to select a subset of images from the target domain, which are then annotated and used for supervised domain adapta…
Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining
Soumen Basu, Somanshu Singla, Mayank Gupta +3
Rich temporal information and variations in viewpoints make video data an attractive choice for learning image representations using unsupervised contrastive learning (UCL) techniq…
Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information Fusion
Ashutosh Agarwal, Chetan Arora
Attention-based models such as transformers have shown outstanding performance on dense prediction tasks, such as semantic segmentation, owing to their capability of capturing long…
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions
Ramya S. Hebbalaguppe, Soumya Suvra Goshal, Jatin Prakash +2
Modern deep neural network models are known to erroneously classify out-of-distribution (OOD) test data into one of the in-distribution (ID) training classes with high confidence.…
Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning
Soumen Basu, Mayank Gupta, Pratyaksha Rana +2
We explore the potential of CNN-based models for gallbladder cancer (GBC) detection from ultrasound (USG) images as no prior study is known. USG is the most common diagnostic modal…
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration
Ramya Hebbalaguppe, Jatin Prakash, Neelabh Madan +1
Deep Neural Networks ( DNN s) are known to make overconfident mistakes, which makes their use problematic in safety-critical applications. State-of-the-art ( SOTA ) calibration tec…
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey
Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Imran Razzak +4
A Machine-Critical Application is a system that is fundamentally necessary to the success of specific and sensitive operations such as search and recovery, rescue, military, and em…