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 #5 on Image Classification on Clothing1M (using clean data) (using extra training data)
The spleen is one of the most commonly injured solid organs in blunt abdominal trauma.
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration.
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.
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.
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.
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.
Second, a FusionNet is proposed to take both the binary mask and CT image as input and perform a binary classification.
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.
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.
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.
There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation.