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Found 30 papers, 16 papers with code
Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling
Protein representation learning has primarily benefited from the remarkable development of language models (LMs).
Advancing Radiograph Representation Learning with Masked Record Modeling
Modern studies in radiograph representation learning rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed.
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis
Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for downstream tasks.
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views.
UNet-2022: Exploring Dynamics in Non-isomorphic Architecture
These phenomena indicate the potential of UNet-2022 to become the model of choice for medical image segmentation.
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e. g.,} social network analysis and recommender systems), computer vision (\emph{e. g.,} object detection and point cloud learning), and natural language processing (\emph{e. g.,} relation extraction and sequence learning), to name a few.
ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification
The overall framework, namely as Prototype-aware Contrastive learning (ProCo), is unified as a single-stage pipeline in an end-to-end manner to alleviate the imbalanced problem in medical image classification, which is also a distinct progress than existing works as they follow the traditional two-stage pipeline.
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection
However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected.
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
To improve data efficiency, we propose hierarchically cascaded transformers that exploit intrinsic image structures through spectral tokens pooling and optimize the learnable parameters through latent attribute surrogates.
Ranked #1 on
Few-Shot Learning
on Mini-Imagenet 5-way (1-shot)
(5 way 1~2 shot metric)
Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training
The difficulties in both data acquisition and annotation substantially restrict the sample sizes of training datasets for 3D medical imaging applications.
Ranked #1 on
Medical Object Detection
on DeepLesion
Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports
Pre-training lays the foundation for recent successes in radiograph analysis supported by deep learning.
Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts
From this perspective, we introduce Preservational Learning to reconstruct diverse image contexts in order to preserve more information in learned representations.
nnFormer: Interleaved Transformer for Volumetric Segmentation
Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community.
Ranked #1 on
Medical Image Segmentation
on Synapse
ConvNets vs. Transformers: Whose Visual Representations are More Transferable?
Vision transformers have attracted much attention from computer vision researchers as they are not restricted to the spatial inductive bias of ConvNets.
Bottom-Up Shift and Reasoning for Referring Image Segmentation
In this paper, we tackle the challenge by jointly performing compositional visual reasoning and accurate segmentation in a single stage via the proposed novel Bottom-Up Shift (BUS) and Bidirectional Attentive Refinement (BIAR) modules.
SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation
Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis.
Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation
In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic.
MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures
Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images.
Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling
However, when being used for model training, only the final ground-truth label is utilized, while the critical information contained in the raw multi-rater gradings regarding the image being an easy/hard case is discarded.
A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation
Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people.
Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations
In deep learning era, pretrained models play an important role in medical image analysis, in which ImageNet pretraining has been widely adopted as the best way.
Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation
The effectiveness of our approach has been demonstrated on both facial age and attractiveness estimation tasks.
Ranked #1 on
Age Estimation
on ChaLearn 2016
Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
To bridge the gap between global and local streams, we propose a multi-class attentional region module which aims to make the number of attentional regions as small as possible and keep the diversity of these regions as high as possible.
Ranked #2 on
Multi-Label Classification
on PASCAL VOC 2012
NTIRE 2020 Challenge on NonHomogeneous Dehazing
We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor.
When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets
While practitioners have had an intuitive understanding of these observations, we do a comprehensive emperical analysis and demonstrate that: (1) the gains from SSL techniques over a fully-supervised baseline are smaller when trained from a pre-trained model than when trained from random initialization, (2) when the domain of the source data used to train the pre-trained model differs significantly from the domain of the target task, the gains from SSL are significantly higher and (3) some SSL methods are able to advance fully-supervised baselines (like Pseudo-Label).
Age Estimation Using Expectation of Label Distribution Learning
Age estimation performance has been greatly improved by using convolutional neural network.
Vortex Pooling: Improving Context Representation in Semantic Segmentation
To be specific, our approach outperforms the previous state-of-the-art model named DeepLab v3 by 1. 5% on the PASCAL VOC 2012 val set and 0. 6% on the test set by replacing the Atrous Spatial Pyramid Pooling (ASPP) module in DeepLab v3 with the proposed Vortex Pooling.
Code Attention: Translating Code to Comments by Exploiting Domain Features
Appropriate comments of code snippets provide insight for code functionality, which are helpful for program comprehension.
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
In this paper, we propose Adaptive Feeding (AF) to combine a fast (but less accurate) detector and an accurate (but slow) detector, by adaptively determining whether an image is easy or hard and choosing an appropriate detector for it.
Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition
The difficulty of image recognition has gradually increased from general category recognition to fine-grained recognition and to the recognition of some subtle attributes such as temperature and geolocation.
