Search Results for author:
Found 42 papers, 31 papers with code
Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay
More seriously, data privacy and storage issues may lead to the unavailability of old data when updating the model.
SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation
Our extensive evaluation results reveal that although SAM shows remarkable zero-shot generalization ability with bounding box prompts, it struggles to segment the whole instrument with point-based prompts and unprompted settings.
Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation
This paper proposes a transformer-based landmark detection solution with deformable DeTR and the semantic-aligned-matching module for detecting landmarks in robot-assisted intubation.
Semi-supervised Learning for Segmentation of Bleeding Regions in Video Capsule Endoscopy
Notably, we develop the segmentation annotations for this dataset ourselves.
Revisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surgery
We further establish a CL framework on three public surgical datasets in the context of surgical settings that consist of overlapping classes between old and new surgical VQLA tasks.
SimCol3D -- 3D Reconstruction during Colonoscopy Challenge
We show that depth prediction in virtual colonoscopy is robustly solvable, while pose estimation remains an open research question.
Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation
Therefore, Temporal Feature Affinity Learning (TFAL) is devised to indicate possible noisy labels by evaluating the affinity between pixels in two adjacent frames.
CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery
Therefore, a surgical Visual Question Localized-Answering (VQLA) system would be helpful for medical students and junior surgeons to learn and understand from recorded surgical videos.
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process.
Generalizing Surgical Instruments Segmentation to Unseen Domains with One-to-Many Synthesis
In this work, we mitigate data-related issues by efficiently leveraging minimal source images to generate synthetic surgical instrument segmentation datasets and achieve outstanding generalization performance on unseen real domains.
Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation
Accurate and robust medical image segmentation is fundamental and crucial for enhancing the autonomy of computer-aided diagnosis and intervention systems.
S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation
Furthermore, to produce reliable mixed pseudo labels, which enhance the effectiveness of ensemble learning, we introduce a novel adaptive pixel-wise fusion technique based on the entropy guidance from the spatial and spectral branches.
Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery
In this paper, we propose Visual Question Localized-Answering in Robotic Surgery (Surgical-VQLA) to localize the specific surgical area during the answer prediction.
Domain Adaptive Sim-to-Real Segmentation of Oropharyngeal Organs Towards Robot-assisted Intubation
Robotic-assisted tracheal intubation requires the robot to distinguish anatomical features like an experienced physician using deep-learning techniques.
Domain Adaptive Sim-to-Real Segmentation of Oropharyngeal Organs
In this work, we propose a domain adaptive Sim-to-Real framework called IoU-Ranking Blend-ArtFlow (IRB-AF) for image segmentation of oropharyngeal organs.
Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective
In this empirical study, we investigate the robustness and zero-shot generalizability of the SAM in the domain of robotic surgery in various settings of (i) prompted vs. unprompted; (ii) bounding box vs. points-based prompt; (iii) generalization under corruptions and perturbations with five severity levels; and (iv) state-of-the-art supervised model vs. SAM.
SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgery
Given the limitations of unidirectional attention in GPT models and their ability to generate coherent long paragraphs, we carefully sequence the word tokens before vision tokens, mimicking the human thought process of understanding the question to infer an answer from an image.
Paced-Curriculum Distillation with Prediction and Label Uncertainty for Image Segmentation
Purpose: In curriculum learning, the idea is to train on easier samples first and gradually increase the difficulty, while in self-paced learning, a pacing function defines the speed to adapt the training progress.
Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding
In this work, we design a paced curriculum by label smoothing (P-CBLS) using paced learning with uniform label smoothing (ULS) for classification tasks and fuse uniform and spatially varying label smoothing (SVLS) for semantic segmentation tasks in a curriculum manner.
Two-stage Contextual Transformer-based Convolutional Neural Network for Airway Extraction from CT Images
In the first stage, the total airway mask and CT images are provided to the subnetwork, and the intrapulmonary airway mask and corresponding CT scans to the subnetwork in the second stage.
Task-Aware Asynchronous Multi-Task Model with Class Incremental Contrastive Learning for Surgical Scene Understanding
Conclusion: The proposed multi-task model was able to adapt to domain shifts, incorporate novel instruments in the target domain, and perform tool-tissue interaction detection and report generation on par with single-task models.
SAVAnet: Surgical Action-Driven Visual Attention Network for Autonomous Endoscope Control
Finally, the simulation results show that the SAVAnet can endow endoscope with surgeons’ visual attention to perform self-control in real time.
Rethinking Surgical Captioning: End-to-End Window-Based MLP Transformer Using Patches
Surgical captioning plays an important role in surgical instruction prediction and report generation.
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.
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.
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.
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.
