From Shapley back to Pearson: Hypothesis Testing via the Shapley Value

The complex nature of artificial neural networks raises concerns on their reliability, trustworthiness, and fairness in real-world scenarios. The Shapley value -- a solution concept from game theory -- is one of the most popular explanation methods for machine learning models. More traditionally, from the perspective of statistical learning, feature importance is defined in terms of conditional independence. So far, these two approaches to interpretability and feature importance have been considered separate and distinct. In this work, we show that Shapley-based explanation methods and conditional independence testing are closely related. We introduce the $\textbf{SHAP}$ley $\textbf{L}$ocal $\textbf{I}$ndependence $\textbf{T}$est ($\textbf{SHAPLIT}$), a novel testing procedure inspired by the Conditional Randomization Test (CRT) for a specific notion of local (i.e., on a sample) conditional independence. With it, we prove that for binary classification problems, each marginal contribution in the Shapley value is an upper bound to the $p$-value of this conditional independence test. Furthermore, we show that the Shapley value itself provides an upper bound to the $p$-value of a global SHAPLIT null hypothesis. As a result, we grant the Shapley value with a precise statistical sense of importance with false positive rate control

CodaMal: Contrastive Domain Adaptation for Malaria Detection in Low-Cost Microscopes

Malaria is a major health issue worldwide, and its diagnosis requires scalable solutions that can work effectively with low-cost microscopes (LCM). Deep learning-based methods have shown success in computer-aided diagnosis from microscopic images. However, these methods need annotated images that show cells affected by malaria parasites and their life stages. Annotating images from LCM significantly increases the burden on medical experts compared to annotating images from high-cost microscopes (HCM). For this reason, a practical solution would be trained on HCM images which should generalize well on LCM images during testing. While earlier methods adopted a multi-stage learning process, they did not offer an end-to-end approach. In this work, we present an end-to-end learning framework, named CodaMal (Contrastive Domain Adpation for Malaria). In order to bridge the gap between HCM (training) and LCM (testing), we propose a domain adaptive contrastive loss. It reduces the domain shift by promoting similarity between the representations of HCM and its corresponding LCM image, without imposing an additional annotation burden. In addition, the training objective includes object detection objectives with carefully designed augmentations, ensuring the accurate detection of malaria parasites. On the publicly available large-scale M5-dataset, our proposed method shows a significant improvement of 16% over the state-of-the-art methods in terms of the mean average precision metric (mAP), provides 21x speed up during inference, and requires only half learnable parameters than the prior methods. Our code is publicly available.Comment: Under Review. Project Page: https://daveishan.github.io/codamal-webpage

Contour Proposal Networks for Biomedical Instance Segmentation

We present a conceptually simple framework for object instance segmentation called Contour Proposal Network (CPN), which detects possibly overlapping objects in an image while simultaneously fitting closed object contours using an interpretable, fixed-sized representation based on Fourier Descriptors. The CPN can incorporate state of the art object detection architectures as backbone networks into a single-stage instance segmentation model that can be trained end-to-end. We construct CPN models with different backbone networks, and apply them to instance segmentation of cells in datasets from different modalities. In our experiments, we show CPNs that outperform U-Nets and Mask R-CNNs in instance segmentation accuracy, and present variants with execution times suitable for real-time applications. The trained models generalize well across different domains of cell types. Since the main assumption of the framework are closed object contours, it is applicable to a wide range of detection problems also outside the biomedical domain. An implementation of the model architecture in PyTorch is freely available

Computer-aided diagnosis systems for automatic malaria parasite detection and classification: a systematic review

Malaria is a disease that affects millions of people worldwide with a consistent mortality rate. The light microscope examination is the gold standard for detecting infection by malaria parasites. Still, it is limited by long timescales and requires a high level of expertise from pathologists. Early diagnosis of this disease is necessary to achieve timely and effective treatment, which avoids tragic consequences, thus leading to the development of computer-aided diagnosis systems based on artificial intelligence (AI) for the detection and classification of blood cells infected with the malaria parasite in blood smear images. Such systems involve an articulated pipeline, culminating in the use of machine learning and deep learning approaches, the main branches of AI. Here, we present a systematic literature review of recent research on the use of automated algorithms to identify and classify malaria parasites in blood smear images. Based on the PRISMA 2020 criteria, a search was conducted using several electronic databases including PubMed, Scopus, and arXiv by applying inclusion/exclusion filters. From the 606 initial records identified, 135 eligible studies were selected and analyzed. Many promising results were achieved, and some mobile and web applications were developed to address resource and expertise limitations in developing countries

Keras R-CNN: library for cell detection in biological images using deep neural networks

Background: A common yet still manual task in basic biology research, high-throughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images. Object detection methods can be useful for identifying individual cells as well as their phenotype in one step. State-of-the-art deep learning for object detection is poised to improve the accuracy and efficiency of biological image analysis. Results: We created Keras R-CNN to bring leading computational research to the everyday practice of bioimage analysts. Keras R-CNN implements deep learning object detection techniques using Keras and Tensorflow (https://github.com/broadinstitute/keras-rcnn). We demonstrate the command line tool’s simplified Application Programming Interface on two important biological problems, nucleus detection and malaria stage classification, and show its potential for identifying and classifying a large number of cells. For malaria stage classification, we compare results with expert human annotators and find comparable performance. Conclusions: Keras R-CNN is a Python package that performs automated cell identification for both brightfield and fluorescence images and can process large image sets. Both the package and image datasets are freely available on GitHub and the Broad Bioimage Benchmark Collection

Класифікація та сегментація медичних зображень за допомогою моделі Detection Transformer (DETR)

Дипломна робота: 150 с., 25 рис., 10 табл., 2 додатки, 84 джерела. Метою роботи є розробка системи автоматизованого аналізу медичних зображень для виявлення клітин, уражених збудником малярії Plasmodium vivax, із використанням моделі Detection Transformer (DETR). У роботі проведено огляд сучасних методів діагностики малярії, зокрема комп’ютерних підходів на основі глибокого навчання. Проаналізовано архітектуру моделі DETR, розглянуто її переваги у задачах об'єктного детектування та сегментації. Особливу увагу приділено підготовці набору даних BBBC041: виконано анотування зображень, створення пайплайну для переведення в формат COCO, а також валідацію структурованих даних. Здійснено донавчання попередньо натренованої моделі DETR на медичному датасеті BBBC041 із налаштуванням гіперпараметрів, також було реалізовано обробку результатів передбачення. Розроблено інструментарій аналізу якості моделі: побудовано графіки функції втрат, точності, метрик AP та приклади передбачень. У фіналі роботи представлено інтерпретацію результатів та сформовано висновки щодо доцільності використання підходу DETR у медичній діагностиці.Diploma Thesis (Bachelor`s Thesis): 150 pages, 25 figures, 10 tables, 2 appendices, 84 references. The aim of this thesis is to develop an automated medical image analysis system for detecting cells infected with the Plasmodium vivax malaria parasite using the Detection Transformer (DETR) model. The work includes an overview of current malaria diagnostic methods, with a focus on computer-based approaches powered by deep learning. The architecture of the DETR model is analyzed, emphasizing its advantages in object detection and segmentation tasks. Special attention is given to the preparation of the BBBC041 dataset: image annotation was performed, a pipeline for conversion to the COCO format was implemented, and the structured data was validated. Fine-tuning of a pretrained DETR model was carried out on the BBBC041 medical dataset with appropriate hyperparameter adjustment, and the prediction results were processed accordingly. An evaluation toolkit was developed, including loss and accuracy plots, AP metrics, and visual examples of predictions. The final part of the thesis presents result interpretation and conclusions regarding the applicability of DETR-based approaches in medical diagnostics