Search Results for author:
Found 20 papers, 15 papers with code
Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need
Our empirical analysis suggests that without the high cost of data collection and annotation, we can achieve decent surgical instrument segmentation performance.
Surgical-VQA: Visual Question Answering in Surgical Scenes using Transformer
This overload often limits their time answering questionnaires from patients, medical students or junior residents related to surgical procedures.
Class Balanced PixelNet for Neurological Image Segmentation
We deal with this problem by selecting an equal number of pixels for all the classes in sampling time.
Brain Tumor Segmentation
Ischemic Stroke Lesion Segmentation
+2
Ischemic Stroke Lesion Segmentation Using Adversarial Learning
Training a segmentation network along with an adversarial network can detect and correct higher order inconsistencies between the segmentation maps produced by ground-truth and the Segmentor.
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding
Global and local relational reasoning enable scene understanding models to perform human-like scene analysis and understanding.
ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery
We generate the task-aware saliency maps and scanpath of the instruments on the dataset of the MICCAI 2017 robotic instrument segmentation challenge.
2020 CATARACTS Semantic Segmentation Challenge
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition
In CDA-TS, the scalar temperature value is replaced with the CDA temperature vector encoded with class frequency to compensate for the over-confidence.
Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation
To adapt incremental classes and extract domain invariant features, a class-incremental (CI) learning method with supervised contrastive (SupCon) loss is incorporated with a feature extractor.
Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations
The task of image segmentation is inherently noisy due to ambiguities regarding the exact location of boundaries between anatomical structures.
Glioma Prognosis: Segmentation of the Tumor and Survival Prediction using Shape, Geometric and Clinical Information
In this paper, we exploit a convolutional neural network (CNN) with hypercolumn technique to segment tumor from healthy brain tissue.
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet
For survival prediction, we extract some novel radiomic features based on geometry, location, the shape of the segmented tumor and combine them with clinical information to estimate the survival duration for each patient.
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction
The proposed approaches are evaluated by comparative experiments with state-of-the-art models in synthesis, segmentation, and overall survival (OS) prediction.
Radiogenomics of Glioblastoma: Identification of Radiomics associated with Molecular Subtypes
Glioblastoma is the most malignant type of central nervous system tumor with GBM subtypes cleaved based on molecular level gene alterations.
Real-Time Instrument Segmentation in Robotic Surgery using Auxiliary Supervised Deep Adversarial Learning
Real-time semantic segmentation of the robotic instruments and tissues is a crucial step in robot-assisted surgery.
Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
Learning to infer graph representations and performing spatial reasoning in a complex surgical environment can play a vital role in surgical scene understanding in robotic surgery.
AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery
Training a real-time robotic system for the detection and segmentation of high-resolution images provides a challenging problem with the limited computational resource.
Learning Where to Look While Tracking Instruments in Robot-assisted Surgery
For this purpose, we propose an end-to-end trainable multitask learning (MTL) model for real-time surgical instrument segmentation and attention prediction.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
