Automated detection of Gallbladder Cancer (GBC) from Ultrasound (US) images
is an important problem, which has drawn increased interest from researchers.
However, most of these works use difficult-to-acquire information such as
bounding box annotations or additional US videos. In this paper, we focus on
GBC detection using only image-level labels. Such annotation is usually
available based on the diagnostic report of a patient, and do not require
additional annotation effort from the physicians. However, our analysis reveals
that it is difficult to train a standard image classification model for GBC
detection. This is due to the low inter-class variance (a malignant region
usually occupies only a small portion of a US image), high intra-class variance
(due to the US sensor capturing a 2D slice of a 3D object leading to large
viewpoint variations), and low training data availability. We posit that even
when we have only the image level label, still formulating the problem as
object detection (with bounding box output) helps a deep neural network (DNN)
model focus on the relevant region of interest. Since no bounding box
annotations is available for training, we pose the problem as weakly supervised
object detection (WSOD). Motivated by the recent success of transformer models
in object detection, we train one such model, DETR, using
multi-instance-learning (MIL) with self-supervised instance selection to suit
the WSOD task. Our proposed method demonstrates an improvement of AP and
detection sensitivity over the SOTA transformer-based and CNN-based WSOD
methods. Project page is at this https URL

通过使用图像级标签进行 GBC 检测，本文提出了一种使用自我监督实例选择的多实例学习方法训练 DETR 模型的弱监督目标检测算法，相较于目前最先进的基于变换器和卷积神经网络的弱监督目标检测方法，我们的方法在 AP 和检测灵敏度上表现出较大的改进。