Digital Imaging for Blood Diseases
Moving in bioRxiv, Cancer Research, medRxiv and PLOS Computational Biology.
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Latest in Digital Imaging for Blood Diseases
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SmartHisto achieves a mean IoU of 0.75 in histology image segmentation, outperforming competitors by over 25% and reducing annotation needs significantly.
- Open access
Counterfactual diffusion models like MoPaDi enable interpretable explanations by revealing morphological features linked to AI predictions in pathology, with 4 datasets spanning multiple cancers.
- Open access
Ensemble U-Net models achieve state-of-the-art segmentation of high-density platelets and organelles in 3D electron microscopy datasets, enabling detailed morphological analysis.
- Open access
- 1 cites
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- bioRxiv5982
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- Sci Adv444
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- Cell Rep238
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- Lancet148
- Science139
- Nature133
- Applied & Environ Microbio117
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- Plant Cell Environ117
- J Exp Bot111
- Curr Biol109
Topics moving, week to 10 Oct 2026
- Cancer Immunotherapy and Biomarkers10
- CAR-T cell therapy research6
- Neuroinflammation and Neurodegeneration Mechanisms6
- Gut microbiota and health6
- Pancreatic and Hepatic Oncology Research5
- Bacteriophages and microbial interactions5
- Single-cell and spatial transcriptomics4
- Cancer Genomics and Diagnostics4
- Functional Brain Connectivity Studies3
- Artificial Intelligence in Healthcare and Education3