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Found 13 papers, 4 papers with code
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.
CATS v2: Hybrid encoders for robust medical segmentation
We fuse the information from the convolutional encoder and the transformer at the skip connections of different resolutions to form the final segmentation.
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation
Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort.
VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation
We map the intensity image and the tensor field to a latent space for feature extraction.
Deep Angiogram: Trivializing Retinal Vessel Segmentation
The generalizability of the synthetic network is improved by the contrastive loss that makes the model less sensitive to variations of image contrast and noisy features.
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.
On Modeling Long-Term User Engagement from Stochastic Feedback
An ultimate goal of recommender systems (RS) is to improve user engagement.
Imaging across multiple spatial scales with the multi-camera array microscope
This article experimentally examines different configurations of a novel multi-camera array microscope (MCAM) imaging technology.
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.
Increasing a microscope's effective field of view via overlapped imaging and machine learning
This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis.
NCS4CVR: Neuron-Connection Sharing for Multi-Task Learning in Video Conversion Rate Prediction
To address this sharing&conflict problem, we propose a novel multi-task CVR modeling scheme with neuron-connection level sharing named NCS4CVR, which can automatically and flexibly learn which neuron weights are shared or not shared without artificial experience.
