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PathoJev: Efficient Pathology Decisions from Shared Visual Context
Chya Zhang, Author 2, Author 3, Author 4, Author 5
multimodalprobabilistic modelingpathologyefficiency
2610.40752
Builder Relevance
1h ago80%
Abstract
PathoJev introduces a model that predicts answer probabilities for pathology assessments directly from shared visual context, reducing latency in decision-making.
Reality Card
Core Claim
PathoJev achieves mean accuracies of 77.2% and 78.1% on whole-slide benchmarks, significantly outperforming SlideChat while being 9.1x faster.
Method / Result
The 9B model runs 9.1x faster than SlideChat.
Limitations
The paper does not specify the dataset diversity or potential biases in the training data.
Paper to code
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