Invasive Coronary Angiography (ICA) images are considered the gold standard
for assessing the state of the coronary arteries. Deep learning classification
methods are widely used and well-developed in different areas where medical
imaging evaluation has an essential impact due to the development of
computer-aided diagnosis systems that can support physicians in their clinical
procedures. In this paper, a new performance analysis of deep learning methods
for binary ICA classification with different lesion degrees is reported. To
reach this goal, an annotated dataset of ICA images that contains the ground
truth, the location of lesions and seven possible severity degrees ranging
between 0% and 100% was employed. The ICA images were divided into 'lesion' or
'non-lesion' patches. We aim to study how binary classification performance is
affected by the different lesion degrees considered in the positive class.
Therefore, five known convolutional neural network architectures were trained
with different input images where different lesion degree ranges were gradually
incorporated until considering the seven lesion degrees. Besides, four types of
experiments with and without data augmentation were designed, whose F-measure
and Area Under Curve (AUC) were computed. Reported results achieved an
F-measure and AUC of 92.7% and 98.1%, respectively. However, lesion
classification is highly affected by the degree of the lesion intended to
classify, with 15% less accuracy when <99% lesion patches are present.

通过深度学习方法及不同程度的病变对冠状动脉侵袭性血管造影图像进行二元分类的新的性能分析研究揭示了病变程度对分类性能的影响。

基于深度学习的冠状动脉疾病不同病变程度分类

Coronary Artery Disease Classification with Different Lesion Degree  Ranges based on Deep Learning

This paper investigates video game identification through single screenshots,
utilizing five convolutional neural network (CNN) architectures (MobileNet,
DenseNet, EfficientNetB0, EfficientNetB2, and EfficientNetB3) across 22 home
console systems, spanning from Atari 2600 to PlayStation 5. Confirming the
hypothesis, CNNs autonomously extract image features, enabling the
identification of game titles from screenshots without additional features.
Using ImageNet pre-trained weights, EfficientNetB3 achieves the highest average
accuracy (74.51%), while DenseNet169 excels in 14 of the 22 systems. Employing
alternative initial weights from another screenshots dataset boosts accuracy
for EfficientNetB2 and EfficientNetB3, with the latter reaching a peak accuracy
of 76.36% and demonstrating reduced convergence epochs from 23.7 to 20.5 on
average. Overall, the combination of optimal architecture and weights attains
77.67% accuracy, primarily led by EfficientNetB3 in 19 systems. These findings
underscore the efficacy of CNNs in video game identification through
screenshots.

通过使用五种卷积神经网络（MobileNet、DenseNet、EfficientNetB0、EfficientNetB2 和 EfficientNetB3），对 22 种家用游戏主机系统（从 Atari 2600 到 PlayStation 5）的单个截图进行视频游戏识别的研究，确认了 CNN 在截图中提取图像特征的能力，实现了在没有额外特征的情况下从截图中识别游戏标题。利用 ImageNet 预训练权重，EfficientNetB3 实现了最高的平均准确性（74.51%），而 DenseNet169 在 22 个系统中的 14 个系统表现出色。使用来自另一个截图数据集的替代初始权重提高了 EfficientNetB2 和 EfficientNetB3 的准确性，后者在平均 23.7 个收敛周期降低到 20.5 个周期，并达到了最高准确性 76.36%。总体而言，通过优化架构和权重的组合，主要由 EfficientNetB3 在 19 个系统中领先，实现了 77.67% 的准确性，这些发现强调了 CNN 在通过截图进行视频游戏识别方面的有效性。

从图像到标题：使用卷积神经网络进行视频游戏辨识

From Pixels to Titles: Video Game Identification by Screenshots using  Convolutional Neural Networks

Automatic border control systems are wide spread in modern airports
worldwide. Morphing attacks on face biometrics is a serious threat that
undermines the security and reliability of face recognition systems deployed in
airports and border controls. Therefore, developing a robust Machine Learning
(ML) system is necessary to prevent criminals crossing borders with fake
identifications especially since it has been shown that security officers
cannot detect morphs better than machines. In this study, we investigate the
generalization power of Convolutional Neural Network (CNN) architectures
against morphing attacks. The investigation utilizes 5 distinct CNNs namely
ShuffleNet, DenseNet201, VGG16, EffecientNet-B0 and InceptionResNet-v2. Each
CNN architecture represents a well-known family of CNN models in terms of
number of parameters, architectural design and performance across various
computer vision applications. To ensure robust evaluation, we employ 4
different datasets (Utrecht, London, Defacto and KurdFace) that contain a
diverse range of digital face images which cover variations in ethnicity,
gender, age, lighting condition and camera setting. One of the fundamental
concepts of ML system design is the ability to generalize effectively to
previously unseen data, hence not only we evaluate the performance of CNN
models within individual datasets but also explore their performance across
combined datasets and investigating each dataset in testing phase only.
Experimental results on more than 8 thousand images (genuine and morph) from
the 4 datasets show that InceptionResNet-v2 generalizes better to unseen data
and outperforms the other 4 CNN models.

开发鲁棒性的机器学习系统是防止犯罪分子使用假身份证件越过边境的必要手段，本研究则调查了卷积神经网络架构对变形攻击的泛化能力，结果发现 InceptionResNet-v2 在对未知数据的泛化效果上优于其他四个 CNN 模型。

卷积神经网络架构在人脸混合造假展示攻击中的通用性

Generalizability of CNN Architectures for Face Morph Presentation Attack

Supervised classification methods often assume that evaluation data is drawn
from the same distribution as training data and that all classes are present
for training. However, real-world classifiers must handle inputs that are far
from the training distribution including samples from unknown classes. Open set
robustness refers to the ability to properly label samples from previously
unseen categories as novel and avoid high-confidence, incorrect predictions.
Existing approaches have focused on either novel inference methods, unique
training architectures, or supplementing the training data with additional
background samples. Here, we propose a simple regularization technique easily
applied to existing convolutional neural network architectures that improves
open set robustness without a background dataset. Our method achieves
state-of-the-art results on open set classification baselines and easily scales
to large-scale open set classification problems.

本文提出了一种新的正则化方法，可应用于现有的卷积神经网络架构中，以提高开放集鲁棒性，实现了最先进的开放集分类基线结果，并轻松扩展到大规模开放集分类问题。