CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels
This work addresses a domain-specific problem for pathologists by automating report generation from multi-image panels, though it appears incremental as it builds on existing attention and RNN techniques.
The paper tackles the problem of generating medical reports for multi-image panels, specifically in Renal Direct Immunofluorescence assays, by proposing an attention-based multi-modal generative RNN that dynamically samples image data and incorporates clinical notes, resulting in significant improvements over existing methods.
This work tackles the problem of generating a medical report for multi-image panels. We apply our solution to the Renal Direct Immunofluorescence (RDIF) assay which requires a pathologist to generate a report based on observations across the eight different WSI in concert with existing clinical features. To this end, we propose a novel attention-based multi-modal generative recurrent neural network (RNN) architecture capable of dynamically sampling image data concurrently across the RDIF panel. The proposed methodology incorporates text from the clinical notes of the requesting physician to regulate the output of the network to align with the overall clinical context. In addition, we found the importance of regularizing the attention weights for word generation processes. This is because the system can ignore the attention mechanism by assigning equal weights for all members. Thus, we propose two regularizations which force the system to utilize the attention mechanism. Experiments on our novel collection of RDIF WSIs provided by a large clinical laboratory demonstrate that our framework offers significant improvements over existing methods.