The segmentation of histopathological images is an important problem in the field of medical image processing. However, the high cost of manual annotation and the lack of large-scale annotated data are important factors that restrict the application of deep learning methods in this field. To overcome these challenges, we propose a two-stage weakly supervised semantic segmentation model based on pathological tissue relationships. Our framework leverages the potential relationships between various tissues in histopathological images through a similar Graph Parsing Attention Mechanism to improve segmentation performance. At the segmentation stage, we validate the effectiveness of our cyclic pseudo-mask strategy for denoising and segmentation, and further enhance segmentation performance through multi-resolution supervision. Our model exhibits advanced performance on both BCSS and LUAD histopathology datasets, demonstrating the superiority of our framework. The contribution of our paper lies in the introduction of prior knowledge about the potential relationships between tissues into the weakly supervised semantic segmentation domain, which realizes high-quality histopathological image segmentation on small sample datasets. Moreover, we propose novel strategies such as cyclic pseudo-masks and multi-resolution supervision to improve segmentation performance. Our framework has significant application value and theoretical significance, providing accurate diagnostic support for doctors.

Introduction Type 2 diabetes mellitus (T2DM) frequently associates with increasing multi-morbidity/treatment complexity. Some headway has been made to identify genetic and non-genetic risk factors for T2DM. However longitudinal clinical histories of individuals both before and after diagnosis of T2DM are likely to provide additional insight into both diabetes aetiology/further complex trajectory of multi-morbidity. Methods This study utilised diabetes patients/controls enrolled in the DARE (Diabetes Alliance for Research in England) study where pre- and post-T2DM diagnosis longitudinal data was available for trajectory analysis. Longitudinal data of 281 individuals (T2DM n=237 vs matched non-T2DM controls n=44) were extracted, checked for errors and logical inconsistencies and then subjected to Trajectory Analysis over a period of up to 70 years based on calculations of the proportions of most prominent clinical conditions for each year. Results For individuals who eventually had a diagnosis of T2DM made, a number of clinical phenotypes were seen to increase consistently in the years leading up to diagnosis of T2DM. Of these documented phenotypes, the most striking were diagnosed hypertension (more than in the control group) and asthma. This trajectory over time was much less dramatic in the matched control group. Immediately prior to T2DM diagnosis a greater indication of ischaemic heart disease proportions was observed. Post-T2DM diagnosis, the proportions of T2DM patients exhibiting hypertension and infection continued to climb rapidly before plateauing. Ischaemic heart disease continued to increase in this group as well as retinopathy, impaired renal function and heart failure. Conclusion These observations provide an intriguing and novel insight into the onset and natural progression of T2DM. They suggest an early phase of potentially-related disease activity well before any clinical diagnosis of diabetes is made. Further studies on a larger cohort of DARE patients are underway to explore the utility of establishing predictive risk scores.

Our analysis as described in this research letter highlights the fact that age outweighs many other factors in people with T2DM in relation to mortality from SARS-CoV-2 virus, once infected. This fact should be taken into account in relation to the vaccination programme against coronavirus-19 in people with T2DM in the UK and elsewhere.