AI2Seg: A Method and Tool for AI-based Annotation Inspection of Biomedical Instance Segmentation Datasets.

In biomedical engineering, deep neural networks are commonly used for the diagnosis and assessment of diseases through the interpretation of medical images. The effectiveness of these networks relies heavily on the availability of annotated datasets for training. However, obtaining noise-free and consistent annotations from experts, such as pathologists, radiologists, and biologists, remains a significant challenge. One common task in clinical practice and biological imaging applications is instance segmentation. Though, there is currently a lack of methods and open-source tools for the automated inspection of biomedical instance segmentation datasets concerning noisy annotations. To address this issue, we propose a novel deep learning-based approach for inspecting noisy annotations and provide an accompanying software implementation, AI2Seg, to facilitate its use by domain experts. The performance of the proposed algorithm is demonstrated on the medical MoNuSeg dataset and the biological LIVECell dataset.

Face2PPG: An Unsupervised Pipeline for Blood Volume Pulse Extraction From Faces.

Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes a set of pipelines to extract remote PPG signals (rPPG) from the face robustly, reliably, and configurably. We identify and evaluate the possible choices in the critical steps of unsupervised rPPG methodologies. We assess a state-of-the-art processing pipeline in six different datasets, incorporating important corrections in the methodology that ensure reproducible and fair comparisons. In addition, we extend the pipeline by proposing three novel ideas; 1) a new method to stabilize the detected face based on a rigid mesh normalization; 2) a new method to dynamically select the different regions in the face that provide the best raw signals, and 3) a new RGB to rPPG transformation method, called Orthogonal Matrix Image Transformation (OMIT) based on QR decomposition, that increases robustness against compression artifacts. We show that all three changes introduce noticeable improvements in retrieving rPPG signals from faces, obtaining state-of-the-art results compared with unsupervised, non-learning-based methodologies and, in some databases, very close to supervised, learning-based methods. We perform a comparative study to quantify the contribution of each proposed idea. In addition, we depict a series of observations that could help in future implementations.

Audio-Visual Kinship Verification: A New Dataset and a Unified Adaptive Adversarial Multimodal Learning Approach.

Facial kinship verification refers to automatically determining whether two people have a kin relation from their faces. It has become a popular research topic due to potential practical applications. Over the past decade, many efforts have been devoted to improving the verification performance from human faces only while lacking other biometric information, for example, speaking voice. In this article, to interpret and benefit from multiple modalities, we propose for the first time to combine human faces and voices to verify kinship, which we refer it as the audio-visual kinship verification study. We first establish a comprehensive audio-visual kinship dataset that consists of familial talking facial videos under various scenarios, called TALKIN-Family. Based on the dataset, we present the extensive evaluation of kinship verification from faces and voices. In particular, we propose a deep-learning-based fusion method, called unified adaptive adversarial multimodal learning (UAAML). It consists of the adversarial network and the attention module on the basis of unified multimodal features. Experiments show that audio (voice) information is complementary to facial features and useful for the kinship verification problem. Furthermore, the proposed fusion method outperforms baseline methods. In addition, we also evaluate the human verification ability on a subset of TALKIN-Family. It indicates that humans have higher accuracy when they have access to both faces and voices. The machine-learning methods could effectively and efficiently outperform the human ability. Finally, we include the future work and research opportunities with the TALKIN-Family dataset.

Facial Kinship Verification: A Comprehensive Review and Outlook.

The goal of Facial Kinship Verification (FKV) is to automatically determine whether two individuals have a kin relationship or not from their given facial images or videos. It is an emerging and challenging problem that has attracted increasing attention due to its practical applications. Over the past decade, significant progress has been achieved in this new field. Handcrafted features and deep learning techniques have been widely studied in FKV. The goal of this paper is to conduct a comprehensive review of the problem of FKV. We cover different aspects of the research, including problem definition, challenges, applications, benchmark datasets, a taxonomy of existing methods, and state-of-the-art performance. In retrospect of what has been achieved so far, we identify gaps in current research and discuss potential future research directions.

A Survey on Computer Vision for Assistive Medical Diagnosis From Faces.

Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patient's condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at upmost importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods. Various approaches have been considered to assess facial symptoms and to eventually provide further help to the practitioners. However, the developed tools are still seldom used in clinical practice, since their reliability is still a concern due to the lack of clinical validation of the methodologies and their inadequate applicability. Nonetheless, efforts are being made to provide robust solutions suitable for healthcare environments, by dealing with practical issues such as real-time assessment or patients positioning. This survey provides an updated collection of the most relevant and innovative solutions in facial images analysis. The findings show that with the help of computer vision methods, over 30 medical conditions can be preliminarily diagnosed from the automatic detection of some of their symptoms. Furthermore, future perspectives, such as the need for interdisciplinary collaboration and collecting publicly available databases, are highlighted.

Comments on the "Kinship Face in the Wild" Data Sets.

The Kinship Face in the Wild data sets, recently published in TPAMI, are currently used as a benchmark for the evaluation of kinship verification algorithms. We recommend that these data sets are no longer used in kinship verification research unless there is a compelling reason that takes into account the nature of the images. We note that most of the image kinship pairs are cropped from the same photographs. Exploiting this cropping information, competitive but biased performance can be obtained using a simple scoring approach, taking only into account the nature of the image pairs rather than any features about kin information. To illustrate our motives, we provide classification results utilizing a simple scoring method based on the image similarity of both images of a kinship pair. Using simply the distance of the chrominance averages of the images in the Lab color space without any training or using any specific kin features, we achieve performance comparable to state-of-the-art methods. We provide the source code to prove the validity of our claims and ensure the repeatability of our experiments.