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Found 20 papers, 16 papers with code
3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding
The remarkable potential of multi-modal large language models (MLLMs) in comprehending both vision and language information has been widely acknowledged.
Post-Deployment Adaptation with Access to Source Data via Federated Learning and Source-Target Remote Gradient Alignment
This paper challenges this assumption and introduces FedPDA, a novel adaptation framework that brings the utility of learning from remote data from Federated Learning into PDA.
Robustness Stress Testing in Medical Image Classification
We conclude that progressive stress testing is a viable and important tool and should become standard practice in the clinical validation of image-based disease detection models.
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation
We demonstrate the effectiveness of our method on four medical image segmentation tasks across different scenarios with two state-of-the-art segmentation models, DeepMedic and nnU-Net.
Multi-source Education Knowledge Graph Construction and Fusion for College Curricula
The field of education has undergone a significant transformation due to the rapid advancements in Artificial Intelligence (AI).
Boosting Physical Layer Black-Box Attacks with Semantic Adversaries in Semantic Communications
To this end, we present SemBLK, a novel method that can learn to generate destructive physical layer semantic attacks for an ESC system under the black-box setting, where the adversaries are imperceptible to humans.
Context Label Learning: Improving Background Class Representations in Semantic Segmentation
The results demonstrate that CoLab can guide the segmentation model to map the logits of background samples away from the decision boundary, resulting in significantly improved segmentation accuracy.
Improved post-hoc probability calibration for out-of-domain MRI segmentation
In image segmentation, well-calibrated probabilities allow radiologists to identify regions where model-predicted segmentations are unreliable.
Tackling Long-Tailed Category Distribution Under Domain Shifts
Two new datasets were proposed for this problem, named AWA2-LTS and ImageNet-LTS.
Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores
If we could estimate the performance that a pre-trained model would achieve on data from a specific deployment setting, for example a certain clinic, we could judge whether the model could safely be deployed or if its performance degrades unacceptably on the specific data.
MaxStyle: Adversarial Style Composition for Robust Medical Image Segmentation
We propose a novel data augmentation framework called MaxStyle, which maximizes the effectiveness of style augmentation for model OOD performance.
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation
Federated distillation (FD) is proposed to simultaneously address the above two problems, which exchanges knowledge between the server and clients, supporting heterogeneous local models while significantly reducing communication overhead.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Causality-inspired Single-source Domain Generalization for Medical Image Segmentation
In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data is only available from one source domain, which is common in medical imaging applications.
Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training.
Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation
In particular, in image segmentation neural networks may overfit to the foreground samples from small structures, which are often heavily under-represented in the training set, leading to poor generalization.
Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition
We hereby propose a decomposition generative adversarial network (DecGAN) to anatomically decompose a CXR image but with unpaired data.
Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation
Overfitting in deep learning has been the focus of a number of recent works, yet its exact impact on the behavior of neural networks is not well understood.
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.
Left Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion
Quantitative assessment of left ventricle (LV) function from cine MRI has significant diagnostic and prognostic value for cardiovascular disease patients.
