#### Weakly-Supervised semantic segmentation for histopathology images based on dataset synthesis and feature consistency constraints --- - homogenity of medical images ![image](https://hackmd.io/_uploads/SkcwqPqDp.png) --- ### WSSS for medical images - multiple instance learning (MIL) - CAM-based methods - HistoSegNet (GradCAM) - WSSS-tissue - C-CAM (MRI) - OEEM --- - **Problems:** though the above methods can alleviate fuzzy boundary problem to some extent, the gap between classification and segmentation still exists --- - PistoSeg framework ![image](https://hackmd.io/_uploads/r1oPhv9wT.png) --- - pseudo-mask refining module ![image](https://hackmd.io/_uploads/rJlCRD9wT.png) ![image](https://hackmd.io/_uploads/BJBe1d9v6.png) --- - Loss design ![image](https://hackmd.io/_uploads/ry8ckOqD6.png) ![image](https://hackmd.io/_uploads/HkcjJdcP6.png) ![image](https://hackmd.io/_uploads/SylCJd9va.png) --- - performance on WSSS4LUAD / BCSS-WSSS ![image](https://hackmd.io/_uploads/H1-BpDqPa.png) --- - ablations on losses ![image](https://hackmd.io/_uploads/rJZnTv9wa.png) --- - ablations on refining module on different CAMs ![image](https://hackmd.io/_uploads/SybCaD5v6.png) --- - ablations on precise segmentation inputs ![image](https://hackmd.io/_uploads/B1cJAP9Pp.png) --- ---
{"title":"Weakly-Supervised semantic segmentation for histopathology images based on dataset synthesis and feature consistency","description":"type: slide","contributors":"[{\"id\":\"4724c2c9-74e7-418c-87c3-79a619b97461\",\"add\":1477,\"del\":95}]"}
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