  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.
