# Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation

arXiv: https://arxiv.org/abs/2409.18506 · HTML: https://mlsanity.com/paper/2409.18506/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 68 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [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)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [UNeXt: MLP-based Rapid Medical Image Segmentation Network](https://mlsanity.com/paper/2203.04967/) (2022)
- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)
- [Involution: Inverting the Inherence of Convolution for Visual Recognition](https://mlsanity.com/paper/2103.06255/) (2021)
- [USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis](https://mlsanity.com/paper/2401.00153/) (2023)
- [MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.07451/) (2021)
- [CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects](https://mlsanity.com/paper/2301.13366/) (2023)
- [Using DUCK-Net for Polyp Image Segmentation](https://mlsanity.com/paper/2311.02239/) (2023)
- [Computational Pathology: A Survey Review and The Way Forward](https://mlsanity.com/paper/2304.05482/) (2023)
- [Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?](https://mlsanity.com/paper/2308.06623/) (2023)
- [Soft-Attention Improves Skin Cancer Classification Performance](https://mlsanity.com/paper/2105.03358/) (2021)
- [Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation](https://mlsanity.com/paper/2207.07842/) (2022)
- [EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified Decoder](https://mlsanity.com/paper/2407.16298/) (2024)
- [Spatially Exclusive Pasting: A General Data Augmentation for the Polyp Segmentation](https://mlsanity.com/paper/2211.08284/) (2022)
- [Involution Fused ConvNet for Classifying Eye-Tracking Patterns of Children with Autism Spectrum Disorder](https://mlsanity.com/paper/2401.03575/) (2024)
- [Method to Classify Skin Lesions using Dermoscopic images](https://mlsanity.com/paper/2008.09418/) (2020)
- [GIU-GANs: Global Information Utilization for Generative Adversarial Networks](https://mlsanity.com/paper/2201.10471/) (2022)

## Built on by (most cited first, 1 of 1)

- [Involution-Infused DenseNet with Two-Step Compression for Resource-Efficient Plant Disease Classification](https://mlsanity.com/paper/2506.00735/) (2025)

## Abstract (arXiv)

The majority of medical images, especially those that resemble cells, have similar characteristics. These images, which occur in a variety of shapes, often show abnormalities in the organ or cell region. The convolution operation possesses a restricted capability to extract visual patterns across several spatial regions of an image. The involution process, which is the inverse operation of convolution, complements this inherent lack of spatial information extraction present in convolutions. In this study, we investigate how applying a single layer of involution prior to a convolutional neural network (CNN) architecture can significantly improve classification and segmentation performance, with a comparatively negligible amount of weight parameters. The study additionally shows how excessive use of involution layers might result in inaccurate predictions in a particular type of medical image. According to our findings from experiments, the strategy of adding only a single involution layer before a CNN-based model outperforms most of the previous works.