# Unified Representation Learning for Efficient Medical Image Analysis

arXiv: https://arxiv.org/abs/2006.11223 · HTML: https://mlsanity.com/paper/2006.11223/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Diffusion-Based Approaches in Medical Image Generation and Analysis](https://mlsanity.com/paper/2412.16860/) (2024)
- [Trilateral Attention Network for Real-time Medical Image Segmentation](https://mlsanity.com/paper/2106.09201/) (2021)
- [Multi-loss ensemble deep learning for chest X-ray classification](https://mlsanity.com/paper/2109.14433/) (2021)
- [Deep ensemble learning for segmenting tuberculosis-consistent manifestations in chest radiographs](https://mlsanity.com/paper/2206.06065/) (2022)
- [Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study](https://mlsanity.com/paper/2211.02475/) (2022)
- [Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?](https://mlsanity.com/paper/2301.04032/) (2023)

## Abstract (arXiv)

Medical image analysis typically includes several tasks such as enhancement, segmentation, and classification. Traditionally, these tasks are implemented using separate deep learning models for separate tasks, which is not efficient because it involves unnecessary training repetitions, demands greater computational resources, and requires a relatively large amount of labeled data. In this paper, we propose a multi-task training approach for medical image analysis, where individual tasks are fine-tuned simultaneously through relevant knowledge transfer using a unified modality-specific feature representation (UMS-Rep). We explore different fine-tuning strategies to demonstrate the impact of the strategy on the performance of target medical image tasks. We experiment with different visual tasks (e.g., image denoising, segmentation, and classification) to highlight the advantages offered with our approach for two imaging modalities, chest X-ray and Doppler echocardiography. Our results demonstrate that the proposed approach reduces the overall demand for computational resources and improves target task generalization and performance. Further, our results prove that the performance of target tasks in medical images is highly influenced by the utilized fine-tuning strategy.