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Found 6 papers, 5 papers with code
Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data
In summary, this research proposes an efficient annotation strategy for tumor detection and localization that is less accurate than per-pixel annotations but useful for creating large-scale datasets for screening tumors in various medical modalities.
Early Detection and Localization of Pancreatic Cancer by Label-Free Tumor Synthesis
We further investigate the per-voxel segmentation performance of pancreatic tumors if AI is trained on a combination of CT scans with synthetic tumors and CT scans with annotated large tumors at an advanced stage.
Deep Gradient Learning for Efficient Camouflaged Object Detection
This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD).
Video Polyp Segmentation: A Deep Learning Perspective
We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era.
Ranked #2 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Guidance and Teaching Network for Video Salient Object Detection
Owing to the difficulties of mining spatial-temporal cues, the existing approaches for video salient object detection (VSOD) are limited in understanding complex and noisy scenarios, and often fail in inferring prominent objects.
Progressively Normalized Self-Attention Network for Video Polyp Segmentation
Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features.
Ranked #6 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
