Image copy-move is an operation that replaces one part of the image with
another part of the same image, which can be used for illegal purposes due to
the potential semantic changes. Recent studies have shown that keypoint-based
algorithms achieved excellent and robust localization performance even when
small or smooth tampered areas were involved. However, when the input image is
low-resolution, most existing keypoint-based algorithms are difficult to
generate sufficient keypoints, resulting in more missed detections. In
addition, existing algorithms are usually unable to distinguish between Similar
but Genuine Objects (SGO) images and tampered images, resulting in more false
alarms. This is mainly due to the lack of further verification of local
homography matrix in forgery localization stage. To tackle these problems, this
paper firstly proposes an excessive keypoint extraction strategy to overcome
missed detection. Subsequently, a group matching algorithm is used to speed up
the matching of excessive keypoints. Finally, a new iterative forgery
localization algorithm is introduced to quickly form pixel-level localization
results while ensuring a lower false alarm. Extensive experimental results show
that our scheme has superior performance than state-of-the-art algorithms in
overcoming missed detection and false alarm. Our code is available at
this https URL

本文提出了过量关键点提取策略以克服遗漏检测，使用分组匹配算法加速匹配过程，引入新的迭代伪造定位算法以快速形成像素级定位结果并确保较低虚警。广泛的实验结果表明，我们的方案在克服遗漏检测和虚警方面具有卓越性能。

图像抄袭移动伪造检测与定位方案：如何避免漏检和虚警

Image Copy-Move Forgery Detection and Localization Scheme: How to Avoid  Missed Detection and False Alarm

Colonoscopy is the most widely used medical technique for preventing
Colorectal Cancer, by detecting and removing polyps before they become
malignant. Recent studies show that around one quarter of the existing polyps
are routinely missed. While some of these do appear in the endoscopist's field
of view, others are missed due to a partial coverage of the colon. The task of
detecting and marking unseen regions of the colon has been addressed in recent
work, where the common approach is based on dense 3D reconstruction, which
proves to be challenging due to lack of 3D ground truth and periods with poor
visual content. In this paper we propose a novel and complementary method to
detect deficient local coverage in real-time for video segments where a
reliable 3D reconstruction is impossible. Our method aims to identify skips
along the colon caused by a drifted position of the endoscope during poor
visibility time intervals. The proposed solution consists of two phases. During
the first, time segments with good visibility of the colon and gaps between
them are identified. During the second phase, a trained model operates on each
gap, answering the question: Do you observe the same scene before and after the
gap? If the answer is negative, the endoscopist is alerted and can be directed
to the appropriate area in real-time. The second phase model is trained using a
contrastive loss based on auto-generated examples. Our method evaluation on a
dataset of 250 procedures annotated by trained physicians provides sensitivity
of 0.75 with specificity of 0.9.

本论文提出一种新方法，实时检测内窥镜检查中因视觉差而被忽略的结肠区域，从而检测不到的息肉。研究采用二阶段方法，第一阶段确定视觉好的区域和之间的缺口，第二阶段使用受过训练的模型来检测缺口，并提供针对性的内窥镜检查。方法评估表明具有很高的敏感性和特异性，可以缩短肠镜检查时间并提高检出率。

结肠镜检查覆盖范围重视：实时识别扫描漏洞

Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time

Prevalence of gastrointestinal (GI) cancer is growing alarmingly every year
leading to a substantial increase in the mortality rate. Endoscopic detection
is providing crucial diagnostic support, however, subtle lesions in upper and
lower GI are quite hard to detect and cause considerable missed detection. In
this work, we leverage deep learning to develop a framework to improve the
localization of difficult to detect lesions and minimize the missed detection
rate. We propose an end to end student-teacher learning setup where class
probabilities of a trained teacher model on one class with larger dataset are
used to penalize multi-class student network. Our model achieves higher
performance in terms of mean average precision (mAP) on both endoscopic disease
detection (EDD2020) challenge and Kvasir-SEG datasets. Additionally, we show
that using such learning paradigm, our model is generalizable to unseen test
set giving higher APs for clinically crucial neoplastic and polyp categories

本文提出了一个利用深度学习来改善诊断失误率的框架，针对胃肠道癌症的诊断，利用端到端的师生学习方式，采用多类别学生网络来利用训练过的师范模型的类别概率，从而提高困难检测病变的定位并最小化漏检率，模型在两个数据集上都实现了更高的 mAP，并且具有泛化性。