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arXiv Machine Learning By Lubin Gan, Jing Zhang, Xinming Zhang, Chengpeng Xu, Siying Wu, Xiaoyan Sun

Beyond the Dominant Label: Recovering Coexisting Lesion Evidence with Multiple Instance Learning

Read the original on arXiv Machine Learning →

The paper introduces DomSet-MIL, a multiple instance learning framework designed to recover evidence of coexisting lesions in whole-slide images when only the most severe diagnosis is weakly supervised. It models the dominant-label mechanism through a censored likelihood that treats the reported diagnosis as the maximum severity without assuming other lesions are absent, and employs severity-ordered residual routing with top‑q pooling to preserve secondary findings. Experiments on natural and controlled datasets show that DomSet-MIL outperforms standard MIL approaches in dominant-label prediction while better recovering latent coexisting-lesion evidence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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