103 citations · 349 across the 55 of their papers we have counts for
9 papers · 1 filter
ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery
Mobarakol Islam, Vibashan VS, Chwee Ming Lim +1
Representation learning of the task-oriented attention while tracking instrument holds vast potential in image-guided robotic surgery. Incorporating cognitive ability to automate t…
2020 CATARACTS Semantic Segmentation Challenge
Imanol Luengo, Maria Grammatikopoulou, Rahim Mohammadi +37
Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In…
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…
Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations
Mobarakol Islam, Ben Glocker
The task of image segmentation is inherently noisy due to ambiguities regarding the exact location of boundaries between anatomical structures. We argue that this information can b…
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet
Mobarakol Islam, Vibashan VS, V Jeya Maria Jose +3
In this work, we develop an attention convolutional neural network (CNN) to segment brain tumors from Magnetic Resonance Images (MRI). Further, we predict the survival rate using v…
Glioma Prognosis: Segmentation of the Tumor and Survival Prediction using Shape, Geometric and Clinical Information
Mobarakol Islam, V Jeya Maria Jose, Hongliang Ren
Segmentation of brain tumor from magnetic resonance imaging (MRI) is a vital process to improve diagnosis, treatment planning and to study the difference between subjects with tumo…
Learning Domain Adaptation with Model Calibration for Surgical Report Generation in Robotic Surgery
Mengya Xu, Mobarakol Islam, Chwee Ming Lim +1
Generating a surgical report in robot-assisted surgery, in the form of natural language expression of surgical scene understanding, can play a significant role in document entry ta…