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Found 29 papers, 13 papers with code
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images
To this end, we propose a Simple Space-Aware Memory Matrix for In-painting and Detecting anomalies from radiography images (abbreviated as SimSID).
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
In this paper, we extend the 2D TransUNet architecture to a 3D network by building upon the state-of-the-art nnU-Net architecture, and fully exploring Transformers' potential in both the encoder and decoder design.
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts
Image synthesis approaches, e. g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks.
Abdominal multi-organ segmentation in CT using Swinunter
Abdominal multi-organ segmentation in computed tomography (CT) is crucial for many clinical applications including disease detection and treatment planning.
Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation
In this work, we propose a spatial-temporal knowledge-embedded transformer (STKET) that incorporates the prior spatial-temporal knowledge into the multi-head cross-attention mechanism to learn more representative relationship representations.
Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation
We aim at incorporating explicit shape information into current 3D organ segmentation models.
Data-Centric Diet: Effective Multi-center Dataset Pruning for Medical Image Segmentation
This paper seeks to address the dense labeling problems where a significant fraction of the dataset can be pruned without sacrificing much accuracy.
Open-World Pose Transfer via Sequential Test-Time Adaption
In open-world conditions, the pose transfer task raises various independent signals: OOD appearance and skeleton, which need to be extracted and distributed in speciality.
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
The proposed model is developed from an assembly of 14 datasets, using a total of 3, 410 CT scans for training and then evaluated on 6, 162 external CT scans from 3 additional datasets.
Ranked #1 on
Organ Segmentation
on BTCV
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
However, we uncover a striking contradiction to this promise: active learning fails to select data as efficiently as random selection at the first few choices.
Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation
Previous methods proposed Variational Autoencoder (VAE) based models to learn the distribution of shape for a particular organ and used it to automatically evaluate the quality of a segmentation prediction by fitting it into the learned shape distribution.
Exploring Negatives in Contrastive Learning for Unpaired Image-to-Image Translation
Recently, contrastive learning (CL) has been used to further investigate the image correspondence in unpaired image translation by using patch-based positive/negative learning.
MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification
However, these approaches formulated skin cancer diagnosis as a simple classification task, dismissing the potential benefit from lesion segmentation.
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients.
Label-Assemble: Leveraging Multiple Datasets with Partial Labels
We discovered that learning from negative examples facilitates both computer-aided disease diagnosis and detection.
Glance-and-Gaze Vision Transformer
It is motivated by the Glance and Gaze behavior of human beings when recognizing objects in natural scenes, with the ability to efficiently model both long-range dependencies and local context.
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.
Ranked #4 on
Medical Image Segmentation
on ACDC
Can Temporal Information Help with Contrastive Self-Supervised Learning?
To this end, we present Temporal-aware Contrastive self-supervised learningTaCo, as a general paradigm to enhance video CSL.
ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation
In this paper, we further improve spatio-temporal point cloud feature learning with a flexible module called ASAP considering both attention and structure information across frames, which we find as two important factors for successful segmentation in dynamic point clouds.
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation
In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains).
Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors
This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans.
Image Generation from Sketch Constraint Using Contextual GAN
We train a generated adversarial network, i. e, contextual GAN to learn the joint distribution of sketch and the corresponding image by using joint images.
Online Video Object Detection Using Association LSTM
Video object detection is a fundamental tool for many applications.
Attribute-Guided Face Generation Using Conditional CycleGAN
We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes.
Beyond Holistic Object Recognition: Enriching Image Understanding with Part States
Important high-level vision tasks such as human-object interaction, image captioning and robotic manipulation require rich semantic descriptions of objects at part level.
Square Localization for Efficient and Accurate Object Detection
In the testing phase, sliding CNN models are applied which produces a set of response maps that can be effectively filtered by the learned co-presence prior to output the final bounding boxes for localizing an object.
Complexity-Adaptive Distance Metric for Object Proposals Generation
Distance metric plays a key role in grouping superpixels to produce object proposals for object detection.
Towards a solid solution of real-time fire and flame detection
Although the object detection and recognition has received growing attention for decades, a robust fire and flame detection method is rarely explored.
Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance
With this graph representation, we pose trajectory analysis as a joint task of spatial graph partitioning and temporal graph matching.
