61 citations · 68 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
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…