# Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers

arXiv: https://arxiv.org/abs/1801.05173 · HTML: https://mlsanity.com/paper/1801.05173/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2018.

## Built on by (most cited first, 30 of 35)

- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining](https://mlsanity.com/paper/2402.03302/) (2024)
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- [Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Images](https://mlsanity.com/paper/2406.00329/) (2024)
- [Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives](https://mlsanity.com/paper/2106.15707/) (2021)
- [Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection](https://mlsanity.com/paper/2201.10166/) (2022)
- [A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images](https://mlsanity.com/paper/2206.03603/) (2022)
- [DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images](https://mlsanity.com/paper/2304.02389/) (2023)
- [MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysis](https://mlsanity.com/paper/2306.17466/) (2023)
- [Merging Context Clustering with Visual State Space Models for Medical Image Segmentation](https://mlsanity.com/paper/2501.01618/) (2025)
- [Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences](https://mlsanity.com/paper/2106.12153/) (2021)
- [Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI](https://mlsanity.com/paper/2203.01089/) (2022)
- [Continuous Spatio-Temporal Memory Networks for 4D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2410.23191/) (2024)
- [Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions](https://mlsanity.com/paper/2105.13153/) (2021)
- [Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2109.12629/) (2021)
- [Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging](https://mlsanity.com/paper/2205.12429/) (2022)
- [Sparse annotation strategies for segmentation of short axis cardiac MRI](https://mlsanity.com/paper/2307.12619/) (2023)
- [A Multi-Scale Spatial Transformer U-Net for Simultaneously Automatic Reorientation and Segmentation of 3D Nuclear Cardiac Images](https://mlsanity.com/paper/2310.10095/) (2023)
- [TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation](https://mlsanity.com/paper/2411.11305/) (2024)
- [Cardiac MRI Semantic Segmentation for Ventricles and Myocardium using Deep Learning](https://mlsanity.com/paper/2504.13391/) (2025)
- [DADU: Dual Attention-based Deep Supervised UNet for Automated Semantic Segmentation of Cardiac Images](https://mlsanity.com/paper/2504.13415/) (2025)
- [MedVKAN: Efficient Feature Extraction with Mamba and KAN for Medical Image Segmentation](https://mlsanity.com/paper/2505.11797/) (2025)
- [Advanced Brain Tumor Segmentation Using EMCAD: Efficient Multi-scale Convolutional Attention Decoding](https://mlsanity.com/paper/2509.05431/) (2025)
- [Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Data](https://mlsanity.com/paper/2104.12284/) (2021)
- [Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking](https://mlsanity.com/paper/2208.00165/) (2022)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Scoliosis Detection using Deep Neural Network](https://mlsanity.com/paper/2210.17269/) (2022)
- [Automatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning](https://mlsanity.com/paper/2405.14300/) (2024)
- [PMR-Net: Parallel Multi-Resolution Encoder-Decoder Network Framework for Medical Image Segmentation](https://mlsanity.com/paper/2409.12678/) (2024)
- [QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction](https://mlsanity.com/paper/2412.07156/) (2024)

## Abstract (arXiv)

Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitting and poor generalization. In this paper, we present a novel highly parameter and memory efficient FCN based architecture for medical image analysis. We propose a novel up-sampling path which incorporates long skip and short-cut connections to overcome the feature map explosion in FCN like architectures. In order to processes the input images at multiple scales and view points simultaneously, we propose to incorporate Inception module's parallel structures. We also propose a novel dual loss function whose weighting scheme allows to combine advantages of cross-entropy and dice loss. We have validated our proposed network architecture on two publicly available datasets, namely: (i) Automated Cardiac Disease Diagnosis Challenge (ACDC-2017), (ii) Left Ventricular Segmentation Challenge (LV-2011). Our approach in ACDC-2017 challenge stands second place for segmentation and first place in automated cardiac disease diagnosis tasks with an accuracy of 100%. In the LV-2011 challenge our approach attained 0.74 Jaccard index, which is so far the highest published result in fully automated algorithms. From the segmentation we extracted clinically relevant cardiac parameters and hand-crafted features which reflected the clinical diagnostic analysis to train an ensemble system for cardiac disease classification. Our approach combined both cardiac segmentation and disease diagnosis into a fully automated framework which is computational efficient and hence has the potential to be incorporated in computer-aided diagnosis (CAD) tools for clinical application.