Occlusion artifacts significantly hinder light field (LF) image reconstruction, especially in complex scenes. We propose a spectral normalized U-Net for LF occlusion removal, which begins by stacking LF views and extracting view-dependent features using a local feature encoder. To capture spatial complexity, ResASPP enable multi-scale context aggregation, while channel attention enhances occlusion-related features. Spectral normalization is applied to all convolutional layers to improve training stability and generalization. The encoder-decoder structure with skip connections preserves fine details. Experimental results show our method restores occluded regions more accurately than baselines.

Occlusion removal in light-field images remains a significant challenge, particularly when dealing with large occlusions. An architecture based on end-to-end learning is proposed to address this challenge that interactively combines CSPDarknet53 and the bidirectional feature pyramid network for efficient light-field occlusion removal. CSPDarknet53 acts as the backbone, providing robust and rich feature extraction across multiple scales, while the bidirectional feature pyramid network enhances comprehensive feature integration through an advanced multi-scale fusion mechanism. To preserve efficiency without sacrificing the quality of the extracted feature, our model uses separable convolutional blocks. A simple refinement module based on half-instance initialization blocks is integrated to explore the local details and global structures. The network’s multi-perspective approach guarantees almost total occlusion removal, enabling it to handle occlusions of varying sizes or complexity. Numerous experiments were run on sparse and dense datasets with varying degrees of occlusion severity in order to assess the performance. Significant advancements over the current cutting-edge techniques are shown in the findings for the sparse dataset, while competitive results are obtained for the dense dataset.

Accurate and early breast cancer detection is critical for improving patient outcomes. In this study, we propose PatchCascade-ViT, a novel self-supervised Vision Transformer (ViT) framework for automated BI-RADS classification of mammographic images. Unlike conventional deep learning approaches that rely heavily on annotated datasets, PatchCascade-ViT leverages Self Patch-level Supervision (SPS) to learn meaningful mammographic representations from unlabeled data, significantly enhancing classification performance. Our framework operates through a two-stage cascade classification process. In the first stage, the model differentiates non-cancerous from potentially cancerous mammograms using SelfPatch, an innovative self-supervised learning task that enhances patch-level feature learning by enforcing consistency among spatially correlated patches. The second stage refines the classification by distinguishing Scattered Fibroglandular from Heterogeneously and Extremely Dense breast tissue categories, enabling more precise breast cancer risk assessment. To validate the effectiveness of PatchCascade-ViT, we conducted extensive evaluations on a dataset of 4,368 mammograms across three BI-RADS classes. Our method achieved a system sensitivity of 85.01% and an F1-score of 84.90%, outperforming existing deep learning-based approaches. By integrating self-supervised learning with a cascade vision transformer architecture, PatchCascade-ViT reduces reliance on annotated datasets while maintaining high classification accuracy. These findings demonstrate its potential for enhancing breast cancer screening, aiding radiologists in early detection, and improving clinical decision-making.