61 citations · 68 across the 5 of their papers we have counts for
6 papers
FALCON: Few-Shot Adversarial Learning for Cross-Domain Medical Image Segmentation
Abdur R. Fayjie, Pankhi Kashyap, Jutika Borah +1
Precise delineation of anatomical and pathological structures within 3D medical volumes is crucial for accurate diagnosis, effective surgical planning, and longitudinal disease mon…
BelHouse3D: A Benchmark Dataset for Assessing Occlusion Robustness in 3D Point Cloud Semantic Segmentation
Umamaheswaran Raman Kumar, Abdur Razzaq Fayjie, Jurgen Hannaert +1
Large-scale 2D datasets have been instrumental in advancing machine learning; however, progress in 3D vision tasks has been relatively slow. This disparity is largely due to the li…
Predictive uncertainty estimation in deep learning for lung carcinoma classification in digital pathology under real dataset shifts
Abdur R. Fayjie, Jutika Borah, Florencia Carbone +2
Deep learning has shown tremendous progress in a wide range of digital pathology and medical image classification tasks. Its integration into safe clinical decision-making support…
FocDepthFormer: Transformer with latent LSTM for Depth Estimation from Focal Stack
Xueyang Kang, Fengze Han, Abdur R. Fayjie +3
Most existing methods for depth estimation from a focal stack of images employ convolutional neural networks (CNNs) using 2D or 3D convolutions over a fixed set of images. However,…
Semi-supervised few-shot learning for medical image segmentation
Abdur R Feyjie, Reza Azad, Marco Pedersoli +3
Recent years have witnessed the great progress of deep neural networks on semantic segmentation, particularly in medical imaging. Nevertheless, training high-performing models requ…
On the Texture Bias for Few-Shot CNN Segmentation
Reza Azad, Abdur R Fayjie, Claude Kauffman +3
Despite the initial belief that Convolutional Neural Networks (CNNs) are driven by shapes to perform visual recognition tasks, recent evidence suggests that texture bias in CNNs pr…