# Residual-SwinCA-Net: A Channel-Aware Integrated Residual CNN-Swin Transformer for Malignant Lesion Segmentation in BUSI arXiv: https://arxiv.org/abs/2512.08243 ยท HTML: https://mlsanity.com/paper/2512.08243/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 50 references that are in the map. - First posted on arXiv 9 Dec 2025. ## Builds on (most cited first, 27 of 27) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021) - [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014) - [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021) - [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018) - [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018) - [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021) - [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021) - [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021) - [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020) - [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021) - [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021) - [UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer](https://mlsanity.com/paper/2109.04335/) (2021) - [nnFormer: Interleaved Transformer for Volumetric Segmentation](https://mlsanity.com/paper/2109.03201/) (2021) - [LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2107.08623/) (2021) - [Automatic Polyp Segmentation via Multi-scale Subtraction Network](https://mlsanity.com/paper/2108.05082/) (2021) - [SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation](https://mlsanity.com/paper/2004.03696/) (2020) - [AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images](https://mlsanity.com/paper/2204.12077/) (2022) - [Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2104.01896/) (2021) - [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023) - [Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images](https://mlsanity.com/paper/1910.08978/) (2019) - [ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation](https://mlsanity.com/paper/2009.12894/) (2020) - [Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation](https://mlsanity.com/paper/2312.15182/) (2023) - [A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions](https://mlsanity.com/paper/2503.16546/) (2025) - [A Novel Channel Boosted Residual CNN-Transformer with Regional-Boundary Learning for Breast Cancer Detection](https://mlsanity.com/paper/2503.15008/) (2025) - [CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual, Spatial, and Boundary-Aware Learning for Brain Tumor MRI Analysis](https://mlsanity.com/paper/2508.17128/) (2025) ## Built on by (most cited first, 2 of 2) - [RSwinV2-MD: An Enhanced Residual SwinV2 Transformer for Monkeypox Detection from Skin Images](https://mlsanity.com/paper/2601.01835/) (2026) - [TFT-ACB-XML: Decision-Level Integration of Customized Temporal Fusion Transformer and Attention-BiLSTM with XGBoost Meta-Learner for BTC Price Forecasting](https://mlsanity.com/paper/2602.12380/) (2026) ## Abstract (arXiv) A novel deep hybrid Residual-SwinCA-Net segmentation framework is proposed in the study for addressing such challenges by extracting locally correlated and robust features, incorporating residual CNN modules. Furthermore, for learning global dependencies, Swin Transformer blocks are customized using internal residual pathways, which reinforce gradient stability, refine local patterns, and facilitate global feature fusion. Formerly, for enhancing tissue continuity, ultrasound noise suppressions, and accentuating fine structural transitions Laplacian-of-Gaussian regional operator is applied, and for maintaining the morphological integrity of malignant lesion contours, a boundary-oriented operator has been incorporated. Subsequently, a contraction strategy was applied stage-wise by progressively reducing features-map progressively for capturing scale invariance and enhancing the robustness of structural variability. In addition, each decoder level prior augmentation integrates a new Multi-Scale Channel Attention and Squeezing (MSCAS) module. The MSCAS selectively emphasizes encoder salient maps, retains discriminative global context, and complementary local structures with minimal computational cost while suppressing redundant activations. Finally, the Pixel-Attention module encodes class-relevant spatial cues by adaptively weighing malignant lesion pixels while suppressing background interference. The Residual-SwinCA-Net and existing CNNs/ViTs techniques have been implemented on the publicly available BUSI dataset. The proposed Residual-SwinCA-Net framework outperformed and achieved 99.29% mean accuracy, 98.74% IoU, and 0.9041 Dice for breast lesion segmentation. The proposed Residual-SwinCA-Net framework improves the BUSI lesion diagnostic performance and strengthens timely clinical decision-making.