A Unified Multi-Phase CT Synthesis and Classification Framework for
  Kidney Cancer Diagnosis with Incomplete Data
Kwang-Hyun Uhm Seung-Won Jung Moon Hyung Choi Sung-Hoo Hong Sung-Jea Ko
Multi-phase CT is widely adopted for the diagnosis of kidney cancer due to
the complementary information among phases. However, the complete set of
multi-phase CT is often not available in practical clinical applications. In
recent years, there have been some studies to generate the missing modality
image from the available data. Nevertheless, the generated images are not
guaranteed to be effective for the diagnosis task. In this paper, we propose a
unified framework for kidney cancer diagnosis with incomplete multi-phase CT,
which simultaneously recovers missing CT images and classifies cancer subtypes
using the completed set of images. The advantage of our framework is that it
encourages a synthesis model to explicitly learn to generate missing CT phases
that are helpful for classifying cancer subtypes. We further incorporate lesion
segmentation network into our framework to exploit lesion-level features for
effective cancer classification in the whole CT volumes. The proposed framework
is based on fully 3D convolutional neural networks to jointly optimize both
synthesis and classification of 3D CT volumes. Extensive experiments on both
in-house and external datasets demonstrate the effectiveness of our framework
for the diagnosis with incomplete data compared with state-of-the-art
baselines. In particular, cancer subtype classification using the completed CT
data by our method achieves higher performance than the classification using
the given incomplete data.