Litcius/Paper detail

E<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si7.svg" display="inline" id="d1e2477"><mml:msup><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>-MIL: An explainable and evidential multiple instance learning framework for whole slide image classification

Jiangbo Shi, Chen Li, Tieliang Gong, Huazhu Fu

2024Medical Image Analysis12 citationsDOI

Topics & Concepts

InterpretabilityComputer scienceArtificial intelligenceField (mathematics)Process (computing)AlgorithmScalable Vector GraphicsReliability (semiconductor)Machine learningMathematicsPhysicsQuantum mechanicsPower (physics)Operating systemPure mathematicsImage Retrieval and Classification TechniquesColorectal Cancer Screening and DetectionVideo Analysis and Summarization
E<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si7.svg" display="inline" id="d1e2477"><mml:msup><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>-MIL: An explainable and evidential multiple instance learning framework for whole slide image classification | Litcius