Acquiring/Recognising Microscopic Objects TRIZ Cases
- Biomedical Image Survival Scoring Through End-to-End Histology Mapping
EPIC-Survival integrates tile encoding, clustering, and aggregation to derive survival scores from whole-slide biomedical images.
- Anatomically Consistent Embeddings for Multi-Scale Medical Imaging
ACE learns global and local anatomical consistency from unlabeled medical images.
- Apparatus, Method and Computer Program
Multi-threshold distance transforms reveal biomarker structures without image segmentation.
- Cascade Machine Learning for Precise Microscopic Phenotype Detection
Cascade models filter and classify microscopic images for precise phenotype detection.
- Regional Embeddings for Efficient Whole-Slide Classification
Patch and regional embeddings capture spatial context for efficient tumor and gene mutation classification from whole-slide images.
- Pathology Image Recognition with YOLOv5s and Cell Type Scoring
This case uses segmentation, preprocessing, YOLOv5s feature extraction, and cell scoring to reduce subjective pathology diagnosis.
- Multimodal Pair Matching Using Perturbation Scores for Unpaired Data
Separate classifiers compare perturbation scores across modalities to match unpaired samples, reducing sample loss and training overhead.
- Automated In Situ Hybridization Analysis for Objective HER2 Scoring
Digital image registration and automated cell selection support objective HER2 assessment across more cells, reducing manual scoring limits.
- Particle Morphology Classification with Hu Moments and K-Means
Segmented particle imagery feeds Hu invariant moments and K-means clustering to categorize powder shapes for manufacturing analysis.
- Label-Free Dying Cell Phenotype Analysis With ML Encoders
ML encoders classify dying-cell phenotypes without staining or fixation.