The segmentation of optic disc (OD) and optic cup (OC) are used in the automatic diagnosis of glaucoma. However, the spatially ambiguous boundary and semantically uncertain region-of-interest area in pictures may lead to the degradation of the performance of precise segmentation of the OC and OD. Unlike most existing methods, including the variants of CNNs (Convolutional Neural Networks) and U-Net, which limit the contributions of rich global features, we instead propose a hybrid CNN-transformer and polar transformation network, dubbed as Polarformer, which aims to extract discriminative and semantic features for robust OD and OC segmentation. Our Polarformer typically exploits contextualized features among all input units and models the correlation of structural relationships under the paradigm of the transformer backbone. More specifically, our learnable polar transformer module optimizes the polar transformations by sampling images in the Cartesian space and then mapping them back to the polar coordinate system for masked-image reconstruction. Extensive experimental results present that our Polarformer achieves superior performance in comparison to most state-of-the-art methods on three publicly available datasets.

High-resolution online sensors are now common in environmental process systems, yet turning their non-stationary, heterogeneous observation streams into reliable predictions of the underlying process state remains difficult. The statistical distribution of a sensor stream changes over time, the measured variables do not coincide with the state variables of interest, and repeatedly running a mechanistic process model for forward prediction is computationally costly. We present FT-Crossformer, a time–frequency Transformer that acts as a data-driven surrogate between multi-sensor observations and multivariate process-state prediction. To handle distribution shift in the sensor streams, a time-domain distribution-transformation module, together with an inverse-mapping module, performs an affine bias correction that removes per-window non-stationary statistics at the input and restores them at the output, so the gap between training and test distributions is reduced without discarding non-stationary information. We show that this affine correction, including its learnable per-variable scale and shift, acts in the frequency domain on every non-zero frequency component as one common scaling factor that does not depend on the frequency index, so it cannot change the relative magnitudes among the components. A frequency-stability measurement module and a frequency-weighting module therefore re-weight the spectral components of the observation signal so that the stable, task-relevant ones contribute more to the reconstructed signal. The cross-dimension attention of the Crossformer backbone serves as a multi-sensor fusion mechanism that models the dependencies among the measured variables. We validate the method on public benchmark datasets from different domains as a check of generality and, most relevantly, for environmental modeling on two real cases: a wastewater nitrogen-and-phosphorus-removal process and chlorophyll forecasting from an in situ estuary sensor mooring in San Francisco Bay. On the estuary chlorophyll data, which carries a strong train-to-test distribution shift, the full FT-Crossformer demonstrates superior accuracy among the evaluated models at the next-day nowcasting horizon, and an ablation shows that both the time-domain trans- formation and the frequency-domain weighting contribute to this accuracy. FT-Crossformer produces forward predictions from distribution-shifted sensor data with a single fixed-cost forward pass in place of a repeated mechanistic solve, which makes it a practical building block for sensor-data integration and assimilation in environmental process modeling.