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Found 8 papers, 1 papers with code
Rethinking Soft Label in Label Distribution Learning Perspective
The primary goal of training in early convolutional neural networks (CNN) is the higher generalization performance of the model.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization
The trained 3D CNNs are able to segment entire 852 x 852 x 250 voxel 3D volumes in only ~60 seconds, thus hastening the progress towards a deeper understanding of phase transformation phenomena such as dendritic solidification.
PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?
The improvement between video/kinematic-based methods and the uni-modality ones was significant for all of the teams.
Ranked #1 on
Semantic Segmentation
on PETRAW
Kinematic Based Workflow Recognition
Segmentation Based Workflow Recognition
+4
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks
The SSKT is independent of the network structure and dataset, and is trained differently from existing knowledge transfer methods; hence, it has an advantage in that the prior knowledge acquired from various tasks can be naturally transferred during the training process to the target task.
hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries
Localization information for all instruments is provided in the form of a bounding box for object detection.
Rethinking Generalization Performance of Surgical Phase Recognition with Expert-Generated Annotations
For gastrectomy for gastric cancer has more complex twenty-one surgical phases, we generate consensus annotation by the revision process with five specialists.
Semi-Supervised Object Detection with Sparsely Annotated Dataset
However, when training an anchor-based detectors with sparse annotations on an image, effort to find effective positive examples can hinder training performance.
