# A Feature Transfer Enabled Multi-Task Deep Learning Model on Medical Imaging

arXiv: https://arxiv.org/abs/1906.01828 · HTML: https://mlsanity.com/paper/1906.01828/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 14 of 14)

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- [Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis](https://mlsanity.com/paper/2103.03328/) (2021)
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- [Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network](https://mlsanity.com/paper/2203.16288/) (2022)
- [Ultrasound Detection of Subquadricipital Recess Distension](https://mlsanity.com/paper/2211.12089/) (2022)
- [Unlocking the Potential of Medical Imaging with ChatGPT's Intelligent Diagnostics](https://mlsanity.com/paper/2305.07429/) (2023)
- [CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning](https://mlsanity.com/paper/2103.01566/) (2021)
- [SYNCS: Synthetic Data and Contrastive Self-Supervised Training for Central Sulcus Segmentation](https://mlsanity.com/paper/2403.15121/) (2024)
- [Equivariant symmetry-aware head pose estimation for fetal MRI](https://mlsanity.com/paper/2512.04890/) (2025)
- [SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT](https://mlsanity.com/paper/2605.02707/) (2026)
- [GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning](https://mlsanity.com/paper/2608.12185/) (2026)

## Abstract (arXiv)

Object detection, segmentation and classification are three common tasks in medical image analysis. Multi-task deep learning (MTL) tackles these three tasks jointly, which provides several advantages saving computing time and resources and improving robustness against overfitting. However, existing multitask deep models start with each task as an individual task and integrate parallelly conducted tasks at the end of the architecture with one cost function. Such architecture fails to take advantage of the combined power of the features from each individual task at an early stage of the training. In this research, we propose a new architecture, FTMTLNet, an MTL enabled by feature transferring. Traditional transfer learning deals with the same or similar task from different data sources (a.k.a. domain). The underlying assumption is that the knowledge gained from source domains may help the learning task on the target domain. Our proposed FTMTLNet utilizes the different tasks from the same domain. Considering features from the tasks are different views of the domain, the combined feature maps can be well exploited using knowledge from multiple views to enhance the generalizability. To evaluate the validity of the proposed approach, FTMTLNet is compared with models from literature including 8 classification models, 4 detection models and 3 segmentation models using a public full field digital mammogram dataset for breast cancer diagnosis. Experimental results show that the proposed FTMTLNet outperforms the competing models in classification and detection and has comparable results in segmentation.