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Found 18 papers, 9 papers with code
Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis
Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life.
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks.
Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies
OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued considerable interest for its potential in medical applications.
TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios
Multi-object tracking in traffic videos is a crucial research area, offering immense potential for enhancing traffic monitoring accuracy and promoting road safety measures through the utilisation of advanced machine learning algorithms.
SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks
Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes.
SAM-Med3D
These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information.
A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation
To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation.
SAM-Med2D
To bridge this gap, we introduce SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images.
STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training
However, the state-of-the-art models for medical image segmentation are still small-scale, with their parameters only in the tens of millions.
Generative Model Based Noise Robust Training for Unsupervised Domain Adaptation
D-CFA minimizes the domain gap by augmenting the source data with distribution-sampled target features, and trains a noise-robust discriminative classifier by using target domain knowledge from the generative models.
FCN+: Global Receptive Convolution Makes FCN Great Again
The motivation of GRC is that different channels of a convolutional filter can have different grid sampling locations across the whole input feature map.
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.
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning
Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data.
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
The combination of PET-based metabolic and CT-based anatomic information can contribute to better tumor segmentation results.
Robust Target Training for Multi-Source Domain Adaptation
Given any existing fully-trained one-step MSDA model, BORT$^2$ turns it to a labeling function to generate pseudo-labels for the target data and trains a target model using pseudo-labeled target data only.
An evaluation of U-Net in Renal Structure Segmentation
Renal structure segmentation from computed tomography angiography~(CTA) is essential for many computer-assisted renal cancer treatment applications.
Dynamic Instance Domain Adaptation
In this paper, we address both single-source and multi-source UDA from a completely different perspective, which is to view each instance as a fine domain.
Domain Attention Consistency for Multi-Source Domain Adaptation
Importantly, the attention module is supervised by a consistency loss, which is imposed on the distributions of channel attention weights between source and target domains.
