What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioning

• URL: http://arxiv.org/abs/2310.20607v1
• Date: Tue, 31 Oct 2023 16:43:03 GMT
• Title: What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioning
• Authors: Wenkang Qin, Rui Xu, Peixiang Huang, Xiaomin Wu, Heyu Zhang and Lin Luo
• Abstract summary: We propose a new paradigm Subtype-guided Masked Transformer (SGMT) for pathological captioning based on Transformers. An accompanying subtype prediction is introduced into SGMT to guide the training process and enhance the captioning accuracy. Experiments on the PatchGastricADC22 dataset demonstrate that our approach effectively adapts to the task with a transformer-based model.
• Score: 6.496515352848627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pathological captioning of Whole Slide Images (WSIs), though is essential in computer-aided pathological diagnosis, has rarely been studied due to the limitations in datasets and model training efficacy. In this paper, we propose a new paradigm Subtype-guided Masked Transformer (SGMT) for pathological captioning based on Transformers, which treats a WSI as a sequence of sparse patches and generates an overall caption sentence from the sequence. An accompanying subtype prediction is introduced into SGMT to guide the training process and enhance the captioning accuracy. We also present an Asymmetric Masked Mechansim approach to tackle the large size constraint of pathological image captioning, where the numbers of sequencing patches in SGMT are sampled differently in the training and inferring phases, respectively. Experiments on the PatchGastricADC22 dataset demonstrate that our approach effectively adapts to the task with a transformer-based model and achieves superior performance than traditional RNN-based methods. Our codes are to be made available for further research and development.

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