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Titlebook: Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Bio; First MICCAI Worksho Siva Teja Kakile

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51#
發(fā)表于 2025-3-30 11:12:04 | 只看該作者
Fault-Tolerant Digital Microfluidic Biochips2% accuracy compared to pathologists’ annotations. Subsequently, we automatically delineated a stromal boundary and computed the percentage of the boundary area occupied by lymphocytes for each DCIS duct. Finally, we computed TIL score as the average of all duct level scores within the slide. We obs
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發(fā)表于 2025-3-30 14:14:13 | 只看該作者
Thermal Radiomics for Improving the Interpretability of Breast Cancer Detection from Thermal Images are interpretable and play an important role in clinical adaptation. When we tested Thermalytix with these radiomics on thermal data obtained from two different clinical studies involving 717 women, it resulted in an AUROC of 0.944 with a sensitivity and specificity of 90.6% and 85.3%, respectively
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發(fā)表于 2025-3-30 18:39:45 | 只看該作者
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發(fā)表于 2025-3-30 20:42:06 | 只看該作者
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發(fā)表于 2025-3-31 03:34:54 | 只看該作者
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發(fā)表于 2025-3-31 06:49:07 | 只看該作者
Non-fever COVID-19 Detection by Infrared Imagingwithout fever (low risk). The results showed that suspicious and confirmed COVID-19 (+) cases characterized by temperatures below the 37.5 °C fever threshold were identified. Also, average forehead and eye temperatures greater than 37.5 C were not enough to detect fever similarly to the proposed CNN
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發(fā)表于 2025-3-31 10:31:01 | 只看該作者
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發(fā)表于 2025-3-31 16:10:44 | 只看該作者
Counterfactual Image Synthesis for?Discovery of?Personalized Predictive Image Markersle sclerosis (MS) clinical trial magnetic resonance imaging (MRI) dataset of relapsing-remitting (RRMS) patients, we demonstrate that our model produces counterfactuals with changes in imaging features that reflect established clinical markers predictive of future MRI lesional activity at the popula
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發(fā)表于 2025-3-31 18:03:57 | 只看該作者
60#
發(fā)表于 2025-3-31 23:54:53 | 只看該作者
Enhancing Local Context of?Histology Features in?Vision Transformersn two large independent rectal cancer datasets of patients selectively treated with radiotherapy and capecitabine in two UK clinical trials. Experiments demonstrate that both models, PREViT and ClusterViT, show improvements in the prediction over baseline models.
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