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Found 15 papers, 6 papers with code
TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians.
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
In this paper, we present a novel Multi-Granularity Cross-modal Alignment (MGCA) framework for generalized medical visual representation learning by harnessing the naturally exhibited semantic correspondences between medical image and radiology reports at three different levels, i. e., pathological region-level, instance-level, and disease-level.
Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness
This is the question that we address in this work.
Domain Generalization for Medical Image Segmentation via Hierarchical Consistency Regularization
Particularly, for the Extrinsic Consistency, we leverage the knowledge across multiple source domains to enforce data-level consistency.
Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation
In this way, the dual teacher models would transfer acquired inter- and intra-domain knowledge to the student model for further integration and exploitation.
DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets
Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains to make the semantic features more discriminative.
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
The multi-instance learning based on the most discriminative instances can be of great benefit for whole slide gastric image diagnosis.
Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation
Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e. g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity.
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization
To this end, we present a new domain generalization framework that learns how to generalize across domains simultaneously from extrinsic relationship supervision and intrinsic self-supervision for images from multi-source domains.
Ranked #24 on
Domain Generalization
on PACS
Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation
Medical image annotations are prohibitively time-consuming and expensive to obtain.
Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound
In this study, we propose a novel reinforcement learning (RL) framework to automatically localize fetal brain standard planes in 3D US.
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information.
Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation
The cross-domain discrepancy (domain shift) hinders the generalization of deep neural networks to work on different domain datasets. In this work, we present an unsupervised domain adaptation framework, called Boundary and Entropy-driven Adversarial Learning (BEAL), to improve the OD and OC segmentation performance, especially on the ambiguous boundary regions.
Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
In this paper, we present a novel patchbased Output Space Adversarial Learning framework (pOSAL) to jointly and robustly segment the OD and OC from different fundus image datasets.
Ranked #2 on
Optic Disc Segmentation
on REFUGE
