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Found 12 papers, 2 papers with code
Automatically Planning Optimal Parallel Strategy for Large Language Models
The number of parameters in large-scale language models based on transformers is gradually increasing, and the scale of computing clusters is also growing.
CGCOD: Class-Guided Camouflaged Object Detection
It is worth emphasizing that for the first time, we extend the object class annotations on existing COD benchmark datasets, and introduce a flexible framework to improve the performance of the existing COD model under text guidance.
SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation
Deep learning-based medical image segmentation helps assist diagnosis and accelerate the treatment process while the model training usually requires large-scale dense annotation datasets.
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution
Thus, we propose an Implicit Self-Texture Enhancement-based dual-branch framework (ISTE) for arbitrary-scale SR of histopathology images to address this challenge.
O-PRESS: Boosting OCT axial resolution with Prior guidance, Recurrence, and Equivariant Self-Supervision
Optical coherence tomography (OCT) is a noninvasive technology that enables real-time imaging of tissue microanatomies.
keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM
Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering.
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation
Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning.
Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)
In this paper, we propose a novel deep graph matching-based framework for point cloud registration.
Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study
All these factors pose a significant challenge to effective polyp detection in a colonoscopy.
AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy
Though challenging, with the great advances in object detection techniques, automated polyp detection still demonstrates a great potential in reducing the false negative rate while maintaining a high precision.
3D Anchor-Free Lesion Detector on Computed Tomography Scans
The anchor mechanism is removed and lesions are formalized as single keypoints.
Data-driven Flower Petal Modeling with Botany Priors
Each segmented petal is then fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned exemplar petals.
