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Found 2 papers, 2 papers with code
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection
Current polyp detection methods from colonoscopy videos use exclusively normal (i. e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps.
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder
Our UAD approach, the memory-augmented multi-level cross-attentional masked autoencoder (MemMC-MAE), is a transformer-based approach, consisting of a novel memory-augmented self-attention operator for the encoder and a new multi-level cross-attention operator for the decoder.
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