Attention mechanisms, vision transformers, and the architectures replacing convolutions across vision tasks. We unpack how attention is used, misused, and reinvented in current research, from efficient attention branches to Mamba-style state space models.
Analysis by the aitrendblend editorial team · Pillar: Vision transformers and attention · Source paper published August 2025 3D part segmentation SAM2 LoRA adaptation multi-view geometry foundation models GeoSAM2 treats twelve renders of a single 3D object as if they were frames of a short video clip. SAM2 was built to watch a video and […]
GeoSAM2 Turns SAM2 Into a 3D Part Segmentation Tool Read More »
Analysis by the aitrendblend editorial team · Medical imaging AI· Graph Attention Networks Digital Pathology Gene Expression Fusion Lung Cancer Survival Multimodal Learning A pathology slide and a gene expression profile describe the same tumor from two completely different angles. One shows how the tissue is physically organized under a microscope, the other shows which
Graph Attention Fusion of Pathology Images and Gene Expression Predicts Cancer Survival Read More »
Analysis by the aitrendblend editorial team · 14 minute read Computational Pathology Vision Transformer Cancer Grading Ordinal Learning A pathologist looking at two biopsy slides rarely just labels each one and moves on. They also weigh how much worse one sample looks than the other, because that comparison shapes how urgently a patient needs treatment.
DIOR-VIT: Vision Transformers Learn the Order of Cancer Grades Read More »