A New Approach to 3D Scene Understanding: Replacing Heavy Segmentation Models for a 16x Speedup
26 Aug 2025
This research introduces Open-YOLO 3D, a novel method using 2D object detectors for high-speed, open-vocabulary 3D instance segmentation.
Drop the Heavyweights: YOLO‑Based 3D Segmentation Outpaces SAM/CLIP
26 Aug 2025
Open‑YOLO 3D replaces costly SAM/CLIP steps with 2D detection, LG label‑maps, and parallelized visibility, enabling fast and accurate 3D OV segmentation.
Related Work on Closed‑Set 3D Segmentation, Open‑Vocabulary 2D Recognition, and SAM/CLIP‑Based 3D Ap
26 Aug 2025
This section reviews closed‑vocabulary 3D methods, open‑vocabulary 2D recognition, and emerging open‑vocabulary 3D segmentation approaches using SAM/CLIP.
No SAM, No CLIP, No Problem: How Open‑YOLO 3D Segments Faster
26 Aug 2025
Open‑YOLO 3D uses 2D object detection instead of heavy SAM/CLIP for open‑vocabulary 3D segmentation, achieving SOTA results with up to 16× faster inference.
In Cancer Research, AI Models Learn to See What Scientists Might Miss
17 Jul 2025
AI models using attention mechanisms reveal how tumors and TP53 mutations appear in cancer slides, offering fresh insight into cancer morphology.
Why Detecting TP53 Mutations in Digital Slides Remains a Challenge
17 Jul 2025
AI models show limited success in detecting TP53 mutations in digital slides, especially at low magnifications, despite promising results in tumor tasks.
How AI Models Are Rethinking Tumor Detection at the Tissue Level
17 Jul 2025
AI models using sparse attention at 5x magnification show strong tumor detection performance, rivaling state-of-the-art with over 0.9 AUC.
Heatmap and Accuracy Results from Medical Image Classification Models
17 Jul 2025
AI models were tested for tumor and mutation detection. Results show AUCs, ROC curves, and heatmaps across magnification levels.
How Researchers Are Preprocessing Gigabyte-Sized WSIs for Deep Learning
16 Jul 2025
Efficiently preprocess WSIs for deep learning by extracting metadata, filtering patches, and embedding features into lightweight HDF5 files.