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.
How We Trained AI Models to Detect Tumors and Gene Mutations
16 Jul 2025
AI models trained on TCGA BRCA and LUSC slides to detect tumors and gene mutations using multiscale magnification and FFPE/frozen slide strategies.
A Comparative Study of Attention-Based MIL Architectures in Cancer Detection
16 Jul 2025
AMIL and AdMIL use attention pooling in MIL for cancer detection. We compare their architecture and interpretability in medical imaging tasks.
How AI Detects Cancer in Whole Slide Images
16 Jul 2025
AI models using MIL can detect tumors and TP53 mutations from digital pathology slides, even with limited labels, by identifying key cancer morphologies.
New AI Model Shows Resilience Amid Sparse Point Cloud Data
16 Jul 2025
IFRP-T2P proves more accurate than Text2Loc in sparse point clouds, showing robust localization performance even with degraded 3D data.