  Over the past two decades, machine analysis of medical imaging has advanced
rapidly, opening up significant potential for several important medical
applications. As complicated diseases increase and the number of cases rises,
the role of machine-based imaging analysis has become indispensable. It serves
as both a tool and an assistant to medical experts, providing valuable insights
and guidance. A particularly challenging task in this area is lesion
segmentation, a task that is challenging even for experienced radiologists. The
complexity of this task highlights the urgent need for robust machine learning
approaches to support medical staff. In response, we present our novel
solution: the D-TrAttUnet architecture. This framework is based on the
observation that different diseases often target specific organs. Our
architecture includes an encoder-decoder structure with a composite
Transformer-CNN encoder and dual decoders. The encoder includes two paths: the
Transformer path and the Encoders Fusion Module path. The Dual-Decoder
configuration uses two identical decoders, each with attention gates. This
allows the model to simultaneously segment lesions and organs and integrate
their segmentation losses.
  To validate our approach, we performed evaluations on the Covid-19 and Bone
Metastasis segmentation tasks. We also investigated the adaptability of the
model by testing it without the second decoder in the segmentation of glands
and nuclei. The results confirmed the superiority of our approach, especially
in Covid-19 infections and the segmentation of bone metastases. In addition,
the hybrid encoder showed exceptional performance in the segmentation of glands
and nuclei, solidifying its role in modern medical image analysis.
