103 citations · 163 across the 13 of their papers we have counts for
15 papers
Class Balanced PixelNet for Neurological Image Segmentation
Mobarakol Islam, Hongliang Ren
In this paper, we propose an automatic brain tumor segmentation approach (e.g., PixelNet) using a pixel-level convolutional neural network (CNN). The model extracts feature from mu…
Ischemic Stroke Lesion Segmentation Using Adversarial Learning
Mobarakol Islam, N Rajiv Vaidyanathan, V Jeya Maria Jose +1
Ischemic stroke occurs through a blockage of clogged blood vessels supplying blood to the brain. Segmentation of the stroke lesion is vital to improve diagnosis, outcome assessment…
Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding
Lalithkumar Seenivasan, Sai Mitheran, Mobarakol Islam +1
Global and local relational reasoning enable scene understanding models to perform human-like scene analysis and understanding. Scene understanding enables better semantic segmenta…
Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition
Mobarakol Islam, Lalithkumar Seenivasan, Hongliang Ren +1
Despite impressive accuracy, deep neural networks are often miscalibrated and tend to overly confident predictions. Recent techniques like temperature scaling (TS) and label smooth…
Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation
Mengya Xu, Mobarakol Islam, Chwee Ming Lim +1
Generating surgical reports aimed at surgical scene understanding in robot-assisted surgery can contribute to documenting entry tasks and post-operative analysis. Despite the impre…
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction
Mobarakol Islam, Navodini Wijethilake, Hongliang Ren
The accurate prognosis of Glioblastoma Multiforme (GBM) plays an essential role in planning correlated surgeries and treatments. The conventional models of survival prediction rely…