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Found 29 papers, 16 papers with code
Group-On: Boosting One-Shot Segmentation with Supportive Query
One-shot semantic segmentation aims to segment query images given only ONE annotated support image of the same class.
Personalized Heart Disease Detection via ECG Digital Twin Generation
Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention.
Multi-rater Prompting for Ambiguous Medical Image Segmentation
In this paper, we tackle two challenges arisen in multi-rater annotations for medical image segmentation (called ambiguous medical image segmentation): (1) How to train a deep learning model when a group of raters produces a set of diverse but plausible annotations, and (2) how to fine-tune the model efficiently when computation resources are not available for re-training the entire model on a different dataset domain.
PoCo: A Self-Supervised Approach via Polar Transformation Based Progressive Contrastive Learning for Ophthalmic Disease Diagnosis
In this paper, we present a self-supervised method via polar transformation based progressive contrastive learning, called PoCo, for ophthalmic disease diagnosis.
Large Window-based Mamba UNet for Medical Image Segmentation: Beyond Convolution and Self-attention
In this paper, we introduce a Large Window-based Mamba U}-shape Network, or LMa-UNet, for 2D and 3D medical image segmentation.
Making Pre-trained Language Models Great on Tabular Prediction
Condensing knowledge from diverse domains, language models (LMs) possess the capability to comprehend feature names from various tables, potentially serving as versatile learners in transferring knowledge across distinct tables and diverse prediction tasks, but their discrete text representation space is inherently incompatible with numerical feature values in tables.
SERVAL: Synergy Learning between Vertical Models and LLMs towards Oracle-Level Zero-shot Medical Prediction
Recent development of large language models (LLMs) has exhibited impressive zero-shot proficiency on generic and common sense questions.
Unraveling Babel: Exploring Multilingual Activation Patterns within Large Language Models
Recently, large language models (LLMs) have achieved tremendous breakthroughs in the field of language processing, yet their mechanisms in processing multiple languages remain agnostic.
Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification
Deep learning approaches exhibit promising performances on various text tasks.
A Survey on Multimodal Large Language Models for Autonomous Driving
We first introduce the background of Multimodal Large Language Models (MLLMs), the multimodal models development using LLMs, and the history of autonomous driving.
Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models
While techniques such as chain-of-thought (CoT) distillation have displayed promise in distilling LLMs into small language models (SLMs), there is a risk that distilled SLMs may still inherit flawed reasoning and hallucinations from LLMs.
TabCaps: A Capsule Neural Network for Tabular Data Classification with BoW Routing
In this paper, we propose to encapsulate all feature values of a record into vectorial features and process them collectively rather than have to deal with individual ones, which directly captures the representations at the data level and benefits robust performances.
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation
As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images.
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels
Since annotating medical images for segmentation tasks commonly incurs expensive costs, it is highly desirable to design an annotation-efficient method to alleviate the annotation burden.
Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction
In this way, we decompose an ordinal regression task into a series of recursive binary classification steps, so as to subtly distinguish adjacent categories.
PGformer: Proxy-Bridged Game Transformer for Multi-Person Highly Interactive Extreme Motion Prediction
Multi-person motion prediction is a challenging task, especially for real-world scenarios of highly interacted persons.
Cross-Layer Retrospective Retrieving via Layer Attention
Motivated by this, we devise a cross-layer attention mechanism, called multi-head recurrent layer attention (MRLA), that sends a query representation of the current layer to all previous layers to retrieve query-related information from different levels of receptive fields.
ExcelFormer: A Neural Network Surpassing GBDTs on Tabular Data
Though deep neural networks have gained enormous successes in various fields (e. g., computer vision) with supervised learning, they have so far been still trailing after the performances of GBDTs on tabular data.
T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction
Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction.
Robust Training of Graph Neural Networks via Noise Governance
To address these issues, we propose a novel RTGNN (Robust Training of Graph Neural Networks via Noise Governance) framework that achieves better robustness by learning to explicitly govern label noise.
ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases
Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool for heart diseases.
Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network
Automatic ECG classification methods, especially the deep learning based ones, have been proposed to detect cardiac abnormalities using ECG records, showing good potential to improve clinical diagnosis and help early prevention of cardiovascular diseases.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentation
In this paper, we propose a new method called Dilated Transformer, which conducts self-attention for pair-wise patch relations captured alternately in local and global scopes.
DANets: Deep Abstract Networks for Tabular Data Classification and Regression
A special basic block is built using AbstLays, and we construct a family of Deep Abstract Networks (DANets) for tabular data classification and regression by stacking such blocks.
Electrocardio Panorama: Synthesizing New ECG Views with Self-supervision
For the first time, we propose a new concept, Electrocardio Panorama, which allows visualizing ECG signals from any queried viewpoints.
Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods
The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e. g., the Tanner-Whitehouse method) for bone age assessment.
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels
In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities.
A Hierarchical Graph Network for 3D Object Detection on Point Clouds
Next, a new GConv based Proposal Reasoning Module reasons on the proposals considering the global scene semantics, and the bounding boxes are then predicted.
Ranked #16 on
3D Object Detection
on SUN-RGBD val
