Deep Modeling and Interpretation for Bladder Cancer Classification

Deep models based on vision transformer (ViT) and convolutional neural network (CNN) have demonstrated remarkable performance on natural datasets. However, these models may not be similar in medical imaging, where abnormal regions cover only a small portion of the image. This challenge motivates this study to investigate the latest deep models for bladder cancer classification tasks. We propose the following to evaluate these deep models: 1) standard classification using 13 models (four CNNs and eight transormer-based models), 2) calibration analysis to examine if these models are well calibrated for bladder cancer classification, and 3) we use GradCAM++ to evaluate the interpretability of these models for clinical diagnosis. We simulate $\sim 300$ experiments on a publicly multicenter bladder cancer dataset, and the experimental results demonstrate that the ConvNext series indicate limited generalization ability to classify bladder cancer images (e.g., $\sim 60\%$ accuracy). In addition, ViTs show better calibration effects compared to ConvNext and swin transformer series. We also involve test time augmentation to improve the models interpretability. Finally, no model provides a one-size-fits-all solution for a feasible interpretable model. ConvNext series are suitable for in-distribution samples, while ViT and its variants are suitable for interpreting out-of-distribution samples.

GazeFormer-MoE: Context-Aware Gaze Estimation via CLIP and MoE Transformer

We present a semantics modulated, multi scale Transformer for 3D gaze estimation. Our model conditions CLIP global features with learnable prototype banks (illumination, head pose, background, direction), fuses these prototype-enriched global vectors with CLIP patch tokens and high-resolution CNN tokens in a unified attention space, and replaces several FFN blocks with routed/shared Mixture of Experts to increase conditional capacity. Evaluated on MPIIFaceGaze, EYEDIAP, Gaze360 and ETH-XGaze, our model achieves new state of the art angular errors of 2.49°, 3.22°, 10.16°, and 1.44°, demonstrating up to a 64% relative improvement over previously reported results. ablations attribute gains to prototype conditioning, cross scale fusion, MoE and hyperparameter. Our code is publicly available at https://github. com/AIPMLab/Gazeformer.