A CNN classifies lung cytology patches as benign or malignant at 100% sensitivity and 96.4% specificity, then routes to one of two Transformer decoders to generate findings text achieving BLEU-4 of 0.828 on 801 images.
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2 Pith papers cite this work. Polarity classification is still indexing.
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SemanticQA unifies prior multiword expression datasets into a benchmark that reveals substantial performance variation among language models on semantic reasoning tasks.
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Automated Description Generation of Cytologic Findings for Lung Cytological Images Using a Pretrained Vision Model and Dual Text Decoders: Preliminary Study
A CNN classifies lung cytology patches as benign or malignant at 100% sensitivity and 96.4% specificity, then routes to one of two Transformer decoders to generate findings text achieving BLEU-4 of 0.828 on 801 images.
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Revisiting a Pain in the Neck: A Semantic Reasoning Benchmark for Language Models
SemanticQA unifies prior multiword expression datasets into a benchmark that reveals substantial performance variation among language models on semantic reasoning tasks.