专题:Digital Imaging for Blood Diseases

This cluster of papers focuses on the automated analysis of blood cell images, particularly in the context of detecting malaria parasites and classifying leukemia. The research utilizes techniques such as image processing, convolutional neural networks, and machine learning for tasks including white blood cell segmentation, feature extraction, and automated diagnosis from microscopic blood images.
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WBCBENCH 2026: A Challenge for Robust White Blood Cell Classification Under Class Imbalance

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M2Net: Multimodal Multitask Mutual Learning for Anti-VEGF Efficacy Prediction

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CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data

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Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study

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A Unified CNN-Based Instance Segmentation Architecture for Blood Cell Classification and Early Cancer Abnormality Recognition

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Classification of Overlapping Red Blood Cells in Microscopic Blood Smear Images Using Deep Learning

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SwinMamba: A hybrid local–global mamba framework for enhancing semantic segmentation of remotely sensed images

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Multiscale contextual attention network for robust diagnosis of acute lymphoblastic leukemia in blood smears: Implications for clinical adoption and the role of medical science liaisons

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Examination of Blood Cell Images to Identify Nannal/Malaria with EfficientNet Model and Fused Features

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Diversity over scale: Whole-slide image variety enables H&E foundation model training with fewer patches

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