The majority of current systems for automatic diagnosis considers the detection of a unique and previously known pathology. Considering specifically the diagnosis of lesions in the small bowel using endoscopic capsule images, very few consider the possible existence of more than one pathology and when they do, they are mainly detection based systems therefore unable to localize the suspected lesions. Such systems do not fully satisfy the medical community, that in fact needs a system that detects any pathology and eventually more than one, when they coexist. In addition, besides the diagnostic capability of these systems, localizing the lesions in the image has been of great interest to the medical community, mainly for training medical personnel purposes. So, nowadays, the inclusion of the lesion location in automatic diagnostic systems is practically mandatory.

Multi-pathology detection can be seen as a multi-object detection task and as each frame can contain different instances of the same lesion, instance segmentation seems to be appropriate for the purpose. Consequently, we argue that a multi-pathology system benefits from using the instance segmentation approach, since classification and segmentation modules are both required complementing each other in lesion detection and localization. According to our best knowledge such a system does not yet exist for the detection of WCE pathologies.

This paper proposes a multi-pathology system that can be applied to WCE images, which uses the Mask Improved RCNN (MI-RCNN), a new mask subnet scheme which has shown to significantly improve mask predictions of the high performing state-of-the-art Mask-RCNN and PANet systems. A novel training strategy based on the second momentum is also proposed for the first time for training Mask-RCNN and PANet based systems. These approaches were tested using the public database KID, and the included pathologies were bleeding, angioectasias, polyps and inflammatory lesions. Experimental results show significant improvements for the proposed versions, reaching increases of almost 7% over the PANet model when the new proposed training approach was employed.

大多数当前用于自动诊断的系统都考虑了对独特且先前已知的病理学的检测。特别考虑使用内窥镜胶囊图像诊断小肠病变，很少有人考虑可能存在一种以上的病理，当它们出现时，它们主要是基于检测的系统，因此无法定位疑似病变。这样的系统并不能完全满足医学界的需求，事实上，医学界需要一个能够检测任何病理并最终检测到多个病理的系统，当它们共存时。此外，除了这些系统的诊断能力外，定位图像中的病灶也引起了医学界的极大兴趣，主要用于培训医务人员的目的。所以，如今，

多病理检测可以看作是一项多对象检测任务，并且由于每个帧可以包含同一病变的不同实例，因此实例分割似乎适合该目的。因此，我们认为多病理系统受益于使用实例分割方法，因为分类和分割模块都需要在病变检测和定位中相互补充。据我们所知，目前尚不存在这样的系统来检测 WCE 病变。

本文提出了一种可应用于 WCE 图像的多病理学系统，该系统使用掩模改进 RCNN (MI-RCNN)，这是一种新的掩模子网方案，已显示显着改善高性能状态的掩模预测。 -art Mask-RCNN 和 PANet 系统。还首次提出了一种基于第二动量的新训练策略，用于训练基于 Mask-RCNN 和 PANet 的系统。这些方法使用公共数据库 KID 进行了测试，包括的病理是出血、血管扩张、息肉和炎症性病变。实验结果表明，提议的版本有显着改进，在采用新提议的训练方法时，比 PANet 模型增加了近 7%。