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Found 15 papers, 7 papers with code
Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery
For those experiencing severe-to-profound sensorineural hearing loss, the cochlear implant (CI) is the preferred treatment.
DaReNeRF: Direction-aware Representation for Dynamic Scenes
However, the straightforward decomposition of 4D dynamic scenes into multiple 2D plane-based representations proves insufficient for re-rendering high-fidelity scenes with complex motions.
Leveraging sinusoidal representation networks to predict fMRI signals from EEG
The ability to obtain fMRI information from EEG would enable cost-effective, imaging across a wider set of brain regions.
WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation on Surgical Videos with Unknown Camera Parameters
In this work, we aimed to build a self-supervised depth and ego-motion estimation system which can predict not only accurate depth maps and camera pose, but also camera intrinsic parameters.
SAMSNeRF: Segment Anything Model (SAM) Guides Dynamic Surgical Scene Reconstruction by Neural Radiance Field (NeRF)
The accurate reconstruction of surgical scenes from surgical videos is critical for various applications, including intraoperative navigation and image-guided robotic surgery automation.
False Negative/Positive Control for SAM on Noisy Medical Images
The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy.
Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects
This paper proposes a Context Axial Reverse Attention Network (CaraNet) to improve the segmentation performance on small objects compared with several recent state-of-the-art models.
Self-Supervised Surgical Instrument 3D Reconstruction from a Single Camera Image
An accurate 3D surgical instrument model is a prerequisite for precise predictions of the pose and depth of the instrument.
Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation
In contrast to the previous state-of-the-art, we introduce Min-Max Similarity (MMS), a contrastive learning form of dual-view training by employing classifiers and projectors to build all-negative, and positive and negative feature pairs, respectively, to formulate the learning as solving a MMS problem.
CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects
Segmenting medical images accurately and reliably is important for disease diagnosis and treatment.
Ranked #9 on
Medical Image Segmentation
on ETIS-LARIBPOLYPDB
CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation
By comparison, CFPNet-M achieves comparable segmentation results on all five medical datasets with only 0. 65 million parameters, which is about 2% of U-Net, and 8. 8 MB memory.
CFPNet: Channel-wise Feature Pyramid for Real-Time Semantic Segmentation
Based on the CFP module, we built CFPNet for real-time semantic segmentation which applied a series of dilated convolution channels to extract effective features.
Segmentation of Infrared Breast Images Using MultiResUnet Neural Network
It was used to segment the breast area by using a set of breast IR images, collected in our pilot study by imaging breast cancer patients and normal volunteers with a thermal infrared camera (N2 Imager).
DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation
The Convolution Neural Network (CNN) has brought a breakthrough in images segmentation areas, especially, for medical images.
