pwc-archive/papers-with-abstracts huggingface dataset:
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation
Authors: Yu Zhu, Qiang Yang, Li Xu
Date: 2024-05-02 00:00:00
Tasks: Active Learning, Cell Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Cell image segmentation is usually implemented using fully supervised deep learning methods, which heavily rely on extensive annotated training data. Yet, due to the complexity of cell morphology and the requirement for specialized knowledge, pixel-level annotation of cell images has become a highly labor-intensive task. To address the above problems, we propose an active learning framework for cell segmentation using bounding box annotations, which greatly reduces the data annotation cost of cell segmentation algorithms. First, we generate a box-supervised learning method (denoted as YOLO-SAM) by combining the YOLOv8 detector with the Segment Anything Model (SAM), which effectively reduces the complexity of data annotation. Furthermore, it is integrated into an active learning framework that employs the MC DropBlock method to train the segmentation model with fewer box-annotated samples. Extensive experiments demonstrate that our model saves more than ninety percent of data annotation time compared to mask-supervised deep learning methods.
Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving
Authors: Zhenjiang Mao, Dong-You Jhong, Ao Wang, Ivan Ruchkin
Date: 2024-05-02 00:00:00
Tasks: Anomaly Detection, Autonomous Driving, Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Out-of-distribution (OOD) detection is essential in autonomous driving, to determine when learning-based components encounter unexpected inputs. Traditional detectors typically use encoder models with fixed settings, thus lacking effective human interaction capabilities. With the rise of large foundation models, multimodal inputs offer the possibility of taking human language as a latent representation, thus enabling language-defined OOD detection. In this paper, we use the cosine similarity of image and text representations encoded by the multimodal model CLIP as a new representation to improve the transparency and controllability of latent encodings used for visual anomaly detection. We compare our approach with existing pre-trained encoders that can only produce latent representations that are meaningless from the user's standpoint. Our experiments on realistic driving data show that the language-based latent representation performs better than the traditional representation of the vision encoder and helps improve the detection performance when combined with standard representations.
Adapting Self-Supervised Learning for Computational Pathology
Authors: Eric Zimmermann, Neil Tenenholtz, James Hall, George Shaikovski, Michal Zelechowski, Adam Casson, Fausto Milletari, Julian Viret, Eugene Vorontsov, SiQi Liu, Kristen Severson
Date: 2024-05-02 00:00:00
Tasks: Position, Self-Supervised Learning

Self-supervised learning (SSL) has emerged as a key technique for training networks that can generalize well to diverse tasks without task-specific supervision. This property makes SSL desirable for computational pathology, the study of digitized images of tissues, as there are many target applications and often limited labeled training samples. However, SSL algorithms and models have been primarily developed in the field of natural images and whether their performance can be improved by adaptation to particular domains remains an open question. In this work, we present an investigation of modifications to SSL for pathology data, specifically focusing on the DINOv2 algorithm. We propose alternative augmentations, regularization functions, and position encodings motivated by the characteristics of pathology images. We evaluate the impact of these changes on several benchmarks to demonstrate the value of tailored approaches.
ShadowNav: Autonomous Global Localization for Lunar Navigation in Darkness
Authors: Deegan Atha, R. Michael Swan, Abhishek Cauligi, Anne Bettens, Edwin Goh, Dima Kogan, Larry Matthies, Masahiro Ono
Date: 2024-05-02 00:00:00

The ability to determine the pose of a rover in an inertial frame autonomously is a crucial capability necessary for the next generation of surface rover missions on other planetary bodies. Currently, most on-going rover missions utilize ground-in-the-loop interventions to manually correct for drift in the pose estimate and this human supervision bottlenecks the distance over which rovers can operate autonomously and carry out scientific measurements. In this paper, we present ShadowNav, an autonomous approach for global localization on the Moon with an emphasis on driving in darkness and at nighttime. Our approach uses the leading edge of Lunar craters as landmarks and a particle filtering approach is used to associate detected craters with known ones on an offboard map. We discuss the key design decisions in developing the ShadowNav framework for use with a Lunar rover concept equipped with a stereo camera and an external illumination source. Finally, we demonstrate the efficacy of our proposed approach in both a Lunar simulation environment and on data collected during a field test at Cinder Lakes, Arizona.
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layer
Authors: Qiuyu Zhu, Yiwei He
Date: 2024-05-02 00:00:00
Tasks: Dimensionality Reduction, Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Out-of-distribution (OOD) detection, crucial for reliable pattern classification, discerns whether a sample originates outside the training distribution. This paper concentrates on the high-dimensional features output by the final convolutional layer, which contain rich image features. Our key idea is to project these high-dimensional features into two specific feature subspaces, leveraging the dimensionality reduction capacity of the network's linear layers, trained with Predefined Evenly-Distribution Class Centroids (PEDCC)-Loss. This involves calculating the cosines of three projection angles and the norm values of features, thereby identifying distinctive information for in-distribution (ID) and OOD data, which assists in OOD detection. Building upon this, we have modified the batch normalization (BN) and ReLU layer preceding the fully connected layer, diminishing their impact on the output feature distributions and thereby widening the distribution gap between ID and OOD data features. Our method requires only the training of the classification network model, eschewing any need for input pre-processing or specific OOD data pre-tuning. Extensive experiments on several benchmark datasets demonstrates that our approach delivers state-of-the-art performance. Our code is available at https://github.com/Hewell0/ProjOOD.
When a Relation Tells More Than a Concept: Exploring and Evaluating Classifier Decisions with CoReX
Authors: Bettina Finzel, Patrick Hilme, Johannes Rabold, Ute Schmid
Date: 2024-05-02 00:00:00
Tasks: Decision Making, Relation

Explanations for Convolutional Neural Networks (CNNs) based on relevance of input pixels might be too unspecific to evaluate which and how input features impact model decisions. Especially in complex real-world domains like biology, the presence of specific concepts and of relations between concepts might be discriminating between classes. Pixel relevance is not expressive enough to convey this type of information. In consequence, model evaluation is limited and relevant aspects present in the data and influencing the model decisions might be overlooked. This work presents a novel method to explain and evaluate CNN models, which uses a concept- and relation-based explainer (CoReX). It explains the predictive behavior of a model on a set of images by masking (ir-)relevant concepts from the decision-making process and by constraining relations in a learned interpretable surrogate model. We test our approach with several image data sets and CNN architectures. Results show that CoReX explanations are faithful to the CNN model in terms of predictive outcomes. We further demonstrate through a human evaluation that CoReX is a suitable tool for generating combined explanations that help assessing the classification quality of CNNs. We further show that CoReX supports the identification and re-classification of incorrect or ambiguous classifications.
MMIST-ccRCC: A Real World Medical Dataset for the Development of Multi-Modal Systems
Authors: Tiago Mota, M. Rita Verdelho, Alceu Bissoto, Carlos Santiago, Catarina Barata
Date: 2024-05-02 00:00:00
Tasks: Survival Prediction

The acquisition of different data modalities can enhance our knowledge and understanding of various diseases, paving the way for a more personalized healthcare. Thus, medicine is progressively moving towards the generation of massive amounts of multi-modal data (\emph{e.g,} molecular, radiology, and histopathology). While this may seem like an ideal environment to capitalize data-centric machine learning approaches, most methods still focus on exploring a single or a pair of modalities due to a variety of reasons: i) lack of ready to use curated datasets; ii) difficulty in identifying the best multi-modal fusion strategy; and iii) missing modalities across patients. In this paper we introduce a real world multi-modal dataset called MMIST-CCRCC that comprises 2 radiology modalities (CT and MRI), histopathology, genomics, and clinical data from 618 patients with clear cell renal cell carcinoma (ccRCC). We provide single and multi-modal (early and late fusion) benchmarks in the task of 12-month survival prediction in the challenging scenario of one or more missing modalities for each patient, with missing rates that range from 26$\%$ for genomics data to more than 90$\%$ for MRI. We show that even with such severe missing rates the fusion of modalities leads to improvements in the survival forecasting. Additionally, incorporating a strategy to generate the latent representations of the missing modalities given the available ones further improves the performance, highlighting a potential complementarity across modalities. Our dataset and code are available here: https://multi-modal-ist.github.io/datasets/ccRCC
S4: Self-Supervised Sensing Across the Spectrum
Authors: Jayanth Shenoy, Xingjian Davis Zhang, Shlok Mehrotra, Bill Tao, Rem Yang, Han Zhao, Deepak Vasisht
Date: 2024-05-02 00:00:00
Tasks: Segmentation, Time Series

Satellite image time series (SITS) segmentation is crucial for many applications like environmental monitoring, land cover mapping and agricultural crop type classification. However, training models for SITS segmentation remains a challenging task due to the lack of abundant training data, which requires fine grained annotation. We propose S4 a new self-supervised pre-training approach that significantly reduces the requirement for labeled training data by utilizing two new insights: (a) Satellites capture images in different parts of the spectrum such as radio frequencies, and visible frequencies. (b) Satellite imagery is geo-registered allowing for fine-grained spatial alignment. We use these insights to formulate pre-training tasks in S4. We also curate m2s2-SITS, a large-scale dataset of unlabeled, spatially-aligned, multi-modal and geographic specific SITS that serves as representative pre-training data for S4. Finally, we evaluate S4 on multiple SITS segmentation datasets and demonstrate its efficacy against competing baselines while using limited labeled data.
Key Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosis
Authors: Diogo J. Araújo, M. Rita Verdelho, Alceu Bissoto, Jacinto C. Nascimento, Carlos Santiago, Catarina Barata
Date: 2024-05-02 00:00:00
Tasks: All, Diagnostic, Medical Diagnosis, Medical Image Analysis, Multiple Instance Learning

Deep learning models have revolutionized the field of medical image analysis, due to their outstanding performances. However, they are sensitive to spurious correlations, often taking advantage of dataset bias to improve results for in-domain data, but jeopardizing their generalization capabilities. In this paper, we propose to limit the amount of information these models use to reach the final classification, by using a multiple instance learning (MIL) framework. MIL forces the model to use only a (small) subset of patches in the image, identifying discriminative regions. This mimics the clinical procedures, where medical decisions are based on localized findings. We evaluate our framework on two medical applications: skin cancer diagnosis using dermoscopy and breast cancer diagnosis using mammography. Our results show that using only a subset of the patches does not compromise diagnostic performance for in-domain data, compared to the baseline approaches. However, our approach is more robust to shifts in patient demographics, while also providing more detailed explanations about which regions contributed to the decision. Code is available at: https://github.com/diogojpa99/MedicalMultiple-Instance-Learning.
Explaining models relating objects and privacy
Authors: Alessio Xompero, Myriam Bontonou, Jean-Michel Arbona, Emmanouil Benetos, Andrea Cavallaro
Date: 2024-05-02 00:00:00

Accurately predicting whether an image is private before sharing it online is difficult due to the vast variety of content and the subjective nature of privacy itself. In this paper, we evaluate privacy models that use objects extracted from an image to determine why the image is predicted as private. To explain the decision of these models, we use feature-attribution to identify and quantify which objects (and which of their features) are more relevant to privacy classification with respect to a reference input (i.e., no objects localised in an image) predicted as public. We show that the presence of the person category and its cardinality is the main factor for the privacy decision. Therefore, these models mostly fail to identify private images depicting documents with sensitive data, vehicle ownership, and internet activity, or public images with people (e.g., an outdoor concert or people walking in a public space next to a famous landmark). As baselines for future benchmarks, we also devise two strategies that are based on the person presence and cardinality and achieve comparable classification performance of the privacy models.
A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images
Authors: Peilong Wang, Timothy L. Kline, Andy D. Missert, Cole J. Cook, Matthew R. Callstrom, Alex Chan, Robert P. Hartman, Zachary S. Kelm, Panagiotis Korfiatis
Date: 2024-05-02 00:00:00
Tasks: Liver Segmentation, Segmentation

Automated segmentation tools often encounter accuracy and adaptability issues when applied to images of different pathology. The purpose of this study is to explore the feasibility of building a workflow to efficiently route images to specifically trained segmentation models. By implementing a deep learning classifier to automatically classify the images and route them to appropriate segmentation models, we hope that our workflow can segment the images with different pathology accurately. The data we used in this study are 350 CT images from patients affected by polycystic liver disease and 350 CT images from patients presenting with liver metastases from colorectal cancer. All images had the liver manually segmented by trained imaging analysts. Our proposed adaptive segmentation workflow achieved a statistically significant improvement for the task of total liver segmentation compared to the generic single segmentation model (non-parametric Wilcoxon signed rank test, n=100, p-value << 0.001). This approach is applicable in a wide range of scenarios and should prove useful in clinical implementations of segmentation pipelines.
Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey
Authors: Rokas Gipiškis, Chun-Wei Tsai, Olga Kurasova
Date: 2024-05-02 00:00:00
Tasks: image-classification, Image Classification, Image Segmentation, Segmentation, Semantic Segmentation

Artificial Intelligence (XAI) has found numerous applications in computer vision. While image classification-based explainability techniques have garnered significant attention, their counterparts in semantic segmentation have been relatively neglected. Given the prevalent use of image segmentation, ranging from medical to industrial deployments, these techniques warrant a systematic look. In this paper, we present the first comprehensive survey on XAI in semantic image segmentation. This work focuses on techniques that were either specifically introduced for dense prediction tasks or were extended for them by modifying existing methods in classification. We analyze and categorize the literature based on application categories and domains, as well as the evaluation metrics and datasets used. We also propose a taxonomy for interpretable semantic segmentation, and discuss potential challenges and future research directions.
Wildfire Risk Prediction: A Review
Authors: Zhengsen Xu, Jonathan Li, Sibo Cheng, Xue Rui, Yu Zhao, Hongjie He, Linlin Xu
Date: 2024-05-02 00:00:00
Tasks: Deep Learning, Prediction, Time Series Forecasting

Wildfires have significant impacts on global vegetation, wildlife, and humans. They destroy plant communities and wildlife habitats and contribute to increased emissions of carbon dioxide, nitrogen oxides, methane, and other pollutants. The prediction of wildfires relies on various independent variables combined with regression or machine learning methods. In this technical review, we describe the options for independent variables, data processing techniques, models, independent variables collinearity and importance estimation methods, and model performance evaluation metrics. First, we divide the independent variables into 4 aspects, including climate and meteorology conditions, socio-economical factors, terrain and hydrological features, and wildfire historical records. Second, preprocessing methods are described for different magnitudes, different spatial-temporal resolutions, and different formats of data. Third, the collinearity and importance evaluation methods of independent variables are also considered. Fourth, we discuss the application of statistical models, traditional machine learning models, and deep learning models in wildfire risk prediction. In this subsection, compared with other reviews, this manuscript particularly discusses the evaluation metrics and recent advancements in deep learning methods. Lastly, addressing the limitations of current research, this paper emphasizes the need for more effective deep learning time series forecasting algorithms, the utilization of three-dimensional data including ground and trunk fuel, extraction of more accurate historical fire point data, and improved model evaluation metrics.
Reference-Free Image Quality Metric for Degradation and Reconstruction Artifacts
Authors: Han Cui, Alfredo De Goyeneche, Efrat Shimron, Boyuan Ma, Michael Lustig
Date: 2024-05-01 00:00:00
Tasks: Deblurring, Image Deblurring, Image Quality Assessment, Super-Resolution

Image Quality Assessment (IQA) is essential in various Computer Vision tasks such as image deblurring and super-resolution. However, most IQA methods require reference images, which are not always available. While there are some reference-free IQA metrics, they have limitations in simulating human perception and discerning subtle image quality variations. We hypothesize that the JPEG quality factor is representatives of image quality measurement, and a well-trained neural network can learn to accurately evaluate image quality without requiring a clean reference, as it can recognize image degradation artifacts based on prior knowledge. Thus, we developed a reference-free quality evaluation network, dubbed "Quality Factor (QF) Predictor", which does not require any reference. Our QF Predictor is a lightweight, fully convolutional network comprising seven layers. The model is trained in a self-supervised manner: it receives JPEG compressed image patch with a random QF as input, is trained to accurately predict the corresponding QF. We demonstrate the versatility of the model by applying it to various tasks. First, our QF Predictor can generalize to measure the severity of various image artifacts, such as Gaussian Blur and Gaussian noise. Second, we show that the QF Predictor can be trained to predict the undersampling rate of images reconstructed from Magnetic Resonance Imaging (MRI) data.
Improved and Explainable Cervical Cancer Classification using Ensemble Pooling of Block Fused Descriptors
Authors: Saurabh Saini, Kapil Ahuja, Akshat S. Chauhan
Date: 2024-05-01 00:00:00
Tasks: Cancer Classification, feature selection, image-classification, Image Classification

Cervical cancer is the second most common cancer in women and causes high death rates. Earlier models for detecting cervical cancer had limited success. In this work, we propose new models that substantially outperform previous models. Previous studies show that pretrained ResNets extract features from cervical cancer images well. Hence, our first model involves working with three ResNets (50, 101, 152). All the existing works use only the last convolution block of their respective ResNet, which captures abstract features (e.g., shapes, objects). However, we believe that detailed features (e.g., color, edges, texture), coming from earlier convolution blocks, are equally important for cancer (specifically cervical cancer) classification. Since now the number of features become large, we use a novel feature selection technique of Global Max Pooling for detailed features and Global Average Pooling for abstract features. Hence, our second model consists of the resulting Cascaded Block Fused variants of the three ResNets. To improve the performance further, we combine and normalize the features of the three standard ResNets as well as our proposed three Cascaded Block Fused ResNets. This type of combination is also new in cancer classification domain (also in cervical cancer), and results in our third and fourth models, respectively. We use a linear SVM for classification. We exhaustively perform experiments on two public datasets, IARC and AnnoCerv, achieving an average performance of 97.92% and 92.97% surpassing standard ResNets performance of 90.89% and 87.97%, respectively. We outperform the competitive approach available on IARC dataset with an average gain of 13.20%, while no prior competitive work available on AnnoCerv. Additionally, we introduce a novel SHAP+LIME explainability method, accurately identifying the cancerous region in 97% of cases.
Structural Pruning of Pre-trained Language Models via Neural Architecture Search
Authors: Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cedric Archambeau
Date: 2024-05-03 00:00:00
Tasks: GPU, Natural Language Understanding, Neural Architecture Search

Pre-trained language models (PLM), for example BERT or RoBERTa, mark the state-of-the-art for natural language understanding task when fine-tuned on labeled data. However, their large size poses challenges in deploying them for inference in real-world applications, due to significant GPU memory requirements and high inference latency. This paper explores neural architecture search (NAS) for structural pruning to find sub-parts of the fine-tuned network that optimally trade-off efficiency, for example in terms of model size or latency, and generalization performance. We also show how we can utilize more recently developed two-stage weight-sharing NAS approaches in this setting to accelerate the search process. Unlike traditional pruning methods with fixed thresholds, we propose to adopt a multi-objective approach that identifies the Pareto optimal set of sub-networks, allowing for a more flexible and automated compression process.