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Found 14 papers, 4 papers with code
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.
Learning to Bootstrap for Combating Label Noise
Specifically, our method dynamically adjusts the per-sample importance weight between the real observed labels and pseudo-labels, where the weights are efficiently determined in a meta process.
Ranked #8 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data
The spleen is one of the most commonly injured solid organs in blunt abdominal trauma.
Intriguing Findings of Frequency Selection for Image Deblurring
Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image.
Ranked #1 on
Deblurring
on RealBlur-R
SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration.
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Experiments on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset show state-of-the-art performance of the proposed framework on semi-supervised medical image segmentation.
JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans
We extensively evaluate our JSSR system on a large-scale medical image dataset containing 1, 485 patient CT imaging studies of four different phases (i. e., 5, 940 3D CT scans with pathological livers) on the registration, segmentation and synthesis tasks.
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of semantic segmentation, such as autonomous driving and medical image analysis.
Ranked #7 on
Anomaly Detection
on Road Anomaly
(using extra training data)
Deep Distance Transform for Tubular Structure Segmentation in CT Scans
Tubular structure segmentation in medical images, e. g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases.
FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans
Second, a FusionNet is proposed to take both the binary mask and CT image as input and perform a binary classification.
An Alarm System For Segmentation Algorithm Based On Shape Model
Motivated by this, in this paper, we learn a feature space using the shape information which is a strong prior shared among different datasets and robust to the appearance variation of input data. The shape feature is captured using a Variational Auto-Encoder (VAE) network that trained with only the ground truth masks.
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
Meanwhile, a fully-supervised method based on our approach achieved state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework, even when fully supervised training is feasible.
Joint Shape Representation and Classification for Detecting PDAC
Shape representation and classification are performed in a joint manner, both to exploit the knowledge that PDAC often changes the shape of the pancreas and to prevent over-fitting.
Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net
There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation.
