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Found 17 papers, 13 papers with code
ViTamin: Designing Scalable Vision Models in the Vision-Language Era
To this end, we introduce ViTamin, a new vision models tailored for VLMs.
3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge
We identify that the Decoder-only 3D-TransUNet model should offer enhanced efficacy in the segmentation of brain metastases, as indicated by our 5-fold cross-validation on the training set.
Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning
Replay-based methods in class-incremental learning (CIL) have attained remarkable success, as replaying the exemplars of old classes can significantly mitigate catastrophic forgetting.
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.
Compositor: Bottom-up Clustering and Compositing for Robust Part and Object Segmentation
Compositor achieves state-of-the-art performance on PartImageNet and Pascal-Part by outperforming previous methods by around 0. 9% and 1. 3% on PartImageNet, 0. 4% and 1. 7% on Pascal-Part in terms of part and object mIoU and demonstrates better robustness against occlusion by around 4. 4% and 7. 1% on part and object respectively.
Label-Free Liver Tumor Segmentation
We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans.
Ranked #1 on
Tumor Segmentation
on LiTS17
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases.
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.
WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation.
SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching
To overcome these problems, we propose a 3D sphere representation-based center-points matching detection network that is anchor-free and automatically predicts the position, radius, and offset of nodules without the manual design of nodule/anchor parameters.
Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining
(2) The sampled deep vertex features with positional embedding are mapped into a sequential space and decoded by a multilayer perceptron (MLP) for semantic classification.
SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation
Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of applications relied on high-precision pathology image segmentation, such as computational pathology and precision medicine.
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
Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency
In this paper, we propose a novel framework with Uncertainty Rectified Pyramid Consistency (URPC) regularization for semi-supervised NPC GTV segmentation.
Semi-supervised Medical Image Segmentation through Dual-task Consistency
Concretely, we use a dual-task deep network that jointly predicts a pixel-wise segmentation map and a geometry-aware level set representation of the target.
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.
Towards Brain-inspired System: Deep Recurrent Reinforcement Learning for Simulated Self-driving Agent
Because of the tremendous research that focuses on human brains and reinforcement learning, scientists have investigated how robots can autonomously tackle complex tasks in the form of a self-driving agent control in a human-like way.
