  Deep neural networks (DNNs) have promoted the development of computer aided
diagnosis (CAD) systems for fundus diseases, helping ophthalmologists reduce
missed diagnosis and misdiagnosis rate. However, the majority of CAD systems
are data-driven but lack of medical prior knowledge which can be
performance-friendly. In this regard, we innovatively proposed a
human-in-the-loop (HITL) CAD system by leveraging ophthalmologists'
eye-tracking information, which is more efficient and accurate. Concretely, the
HITL CAD system was implemented on the multiple instance learning (MIL), where
eye-tracking gaze maps were beneficial to cherry-pick diagnosis-related
instances. Furthermore, the dual-cross-attention MIL (DCAMIL) network was
utilized to curb the adverse effects of noisy instances. Meanwhile, both
sequence augmentation module and domain adversarial module were introduced to
enrich and standardize instances in the training bag, respectively, thereby
enhancing the robustness of our method. We conduct comparative experiments on
our newly constructed datasets (namely, AMD-Gaze and DR-Gaze), respectively for
the AMD and early DR detection. Rigorous experiments demonstrate the
feasibility of our HITL CAD system and the superiority of the proposed DCAMIL,
fully exploring the ophthalmologists' eye-tracking information. These
investigations indicate that physicians' gaze maps, as medical prior knowledge,
is potential to contribute to the CAD systems of clinical diseases.
