The Cancer Imaging Archive
PulseAugur coverage of The Cancer Imaging Archive — every cluster mentioning The Cancer Imaging Archive across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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New MSR-ABC framework improves ccRCC classification using integrated features
Researchers have developed MSR-ABC, a novel framework for classifying clear cell renal cell carcinoma (ccRCC) from CT scans. This method integrates MedVAE, MedSigLIP, and radiomic features through a global-local archite…
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New lightweight model offers energy-efficient lung tumor segmentation
Researchers have developed a new, energy-efficient model for segmenting lung tumors, crucial for cancer detection and treatment planning. This model, based on the ENet architecture and enhanced with U-Net style skip con…
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Biomedical imaging AI progress stalled by data silos, not model limitations
The primary challenge in advancing biomedical imaging AI is not the development of more sophisticated models, but rather the accessibility and usability of the data itself. Imaging data is often siloed within hospital s…
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Glioblastoma dataset CFB-GBM v2.0 enhanced with complete GTV segmentations
Researchers have released CFB-GBM v2.0, an expanded dataset for glioblastoma research, now including complete Gross Tumour Volume (GTV) delineations for 264 patients. This dataset, available on The Cancer Imaging Archiv…
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New SPARC-Rad benchmark evaluates radiology VLMs for spatial reasoning
Researchers have developed SPARC-Rad, a new benchmark dataset and evaluation pipeline designed to assess the spatial and anatomical reasoning capabilities of vision-language models (VLMs) in the field of radiology. Unli…
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New LaryngealCT Dataset Benchmarks Deep Learning for Cancer Staging
Researchers have developed LaryngealCT, a new benchmark dataset for staging laryngeal cancer using deep learning models. The dataset comprises 1,029 CT scans aggregated from The Cancer Imaging Archive and has been used …
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Medical foundation models lag behind radiomics for renal lesion CT analysis
A new benchmark study evaluated the effectiveness of three medical foundation models (FMs) for stratifying renal lesions in CT scans. While FMs showed promise by matching the performance of a 3D ResNet trained from scra…