69 citations · 85 across the 6 of their papers we have counts for
6 papers
Ambiguous Medical Image Segmentation using Diffusion Models
Aimon Rahman, Jeya Maria Jose Valanarasu, Ilker Hacihaliloglu +1
Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existin…
Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency
Aimon Rahman, Jeya Maria Jose Valanarasu, Ilker Hacihaliloglu +1
Segmenting both bone surface and the corresponding acoustic shadow are fundamental tasks in ultrasound (US) guided orthopedic procedures. However, these tasks are challenging due t…
Orientation-guided Graph Convolutional Network for Bone Surface Segmentation
Aimon Rahman, Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu +2
Due to imaging artifacts and low signal-to-noise ratio in ultrasound images, automatic bone surface segmentation networks often produce fragmented predictions that can hinder the s…
ViPTT-Net: Video pretraining of spatio-temporal model for tuberculosis type classification from chest CT scans
Hasib Zunair, Aimon Rahman, Nabeel Mohammed
Pretraining has sparked groundswell of interest in deep learning workflows to learn from limited data and improve generalization. While this is common for 2D image classification t…
Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction
Hasib Zunair, Aimon Rahman, Nabeel Mohammed +1
A common approach to medical image analysis on volumetric data uses deep 2D convolutional neural networks (CNNs). This is largely attributed to the challenges imposed by the nature…
Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks
Aimon Rahman, Hasib Zunair, M Sohel Rahman +5
Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria det…