Description
Book Synopsis.- Fine-tuning a Multiple Instance Learning Feature Extractor with Masked Context Modelling and Knowledge Distillation.
.- Advancing Medical Radiograph Representation Learning: A Hybrid Pretraining Paradigm with Multilevel Semantic Granularity.
.- Can virtual staining for high-throughput screening generalize?.
.- SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images.
.- A Good Feature Extractor Is All You Need for Weakly Supervised Pathology Slide Classification.
.- Boosting Medical Image Registration Network Inherently via Collaborative Learning.
.- Genetic Information Analysis of Age-Related Macular Degeneration Fellow Eye Using Multi-Modal Selective ViT.
.- CHOTA: A Higher Order Accuracy Metric for Cell Tracking.
.- Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification.
.- Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images.
.- BATseg: Boundary-aware Multiclass Spinal Cord Tumor Segmentation on 3D MRI Scans.
.- Affinity-VAE: incorporating prior knowledge in representation learning from scientific images.
.- Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models.
.- Going Beyond U-Net: Assessing Vision Transformers for Semantic Segmentation in Microscopy Image Analysis.
.- SS-MIL: Attention-Based Selective Correlated Multiple Instance Learning for Whole Slide Image Classification.
.- MicroSSIM: Improved Structured Similarity for Comparing Microscopy Data.
.- Generalized Segmentation for Maxillary Sinus and Mandibular Canal in Dental Panoramic X-rays.
.- MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation.
.- NCT-CRC-HE: Not All Histopathological Datasets Are Equally Useful.
.- Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics.
.- A Novel Approach to Linking Histology Images with DNA Methylation.
