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Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

Histopathological Image Classification with Cell Morphology ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Andrey Ignatov Josephine Yates Valentina Boeva Computer Vision Lab ETH Zurich Computational Cancer Genomics Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Histopathological images are widely used for the analysis of diseased (tumor) tissues and patient treatment selection. While the majority of microscopy image processing was previously done manually by pathologists, recent advances in computer vision allow for accurate recognition of lesion regions with deep learning-based solutions. Such models, however, usually require extensive annotated datasets for training, which is often not the case in the considered task, where the number of available patient data samples is very limited. To deal with this problem, we propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types. The model consists of two modules: the first one performs cell nuclei segmentation and annotates each cell type, and is trained on a combination of 8 publicly available datasets to ensure its high generalizability and robustness. The second module combines the obtained segmentation map with the original microscopy image and is trained for the downstream task. We pre-trained this module on the Pan-Cancer TCGA dataset consisting of over 270K tissue patches extracted from 8736 diagnostic slides from 7175 patients. The proposed solution achieved a new state-of-the-art performance on the dataset under consideration, detecting 32 cancer types with over 82% accuracy and outperforming all previously proposed solutions by more than 4%. We demonstrate that the resulting pre-trained model can be easily fine-tuned on smaller microscopy datasets, yielding superior results compared to the current top solutions and models initialized with ImageNet weights. The codes and pre-trained models presented in this paper are available at: https://***/aiff22/DeepCMorph

关键词： Image segmentation computer vision Analytical models Accuracy Microscopy Microprocessors Computational modeling

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i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?

i-MAE: Are Latent Representations in Masked Autoencoders Lin...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Kevin Zhang Zhiqiang Shen Peking University KNQ.AI Mohamed bin Zayed University of AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Masked image modeling (MIM) has been recognized as a strong self-supervised pre-training approach in the vision domain. However, the mechanism and properties of the learned representations by such a scheme, as well as how to further enhance the representations are so far not well-explored. In this paper, we aim to explore an interactive Masked Autoencoders (i-MAE) framework to enhance the representation capability from two aspects: (1) employing a two-way image reconstruction and a latent feature reconstruction with distillation loss to learn better features; (2) proposing a semantics-enhanced sampling strategy to boost the learned semantics in MAE. Upon the proposed i-MAE architecture, we can address two critical questions to explore the behaviors of the learned representations in MAE: (1) Whether the separability of latent representations in Masked Autoencoders is helpful for model performance? We study it by forcing the input as a mixture of two images instead of one. (2) Whether we can enhance the representations in the latent feature space by controlling the degree of semantics during sampling on Masked Autoencoders? To this end, we propose a sampling strategy within a mini-batch based on the semantics of training samples to examine this aspect. Extensive experiments are conducted on CIFAR-10/100, Tiny-ImageNet and ImageNet-1K datasets to verify the observations we discovered. Furthermore, in addition to qualitatively analyzing the characteristics of the latent representations, we examine the existence of linear separability and the degree of semantics in the latent space by proposing two evaluation schemes. The surprising and consistent results across the qualitative and quantitative experiments demonstrate that i-MAE is a superior framework design for understanding MAE frameworks, as well as achieving better representational ability.

关键词： Training Measurement computer vision Image recognition conferences Semantics Aerospace electronics

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The Third Monocular Depth Estimation Challenge

The Third Monocular Depth Estimation Challenge

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jaime Spencer Fabio Tosi Matteo Poggi Ripudaman Singh Arora Chris Russell Simon Hadfield Richard Bowden GuangYuan Zhou ZhengXin Li Qiang Rao YiPing Bao Xiao Liu Dohyeong Kim Jinseong Kim Myunghyun Kim Mykola Lavreniuk Rui Li Qing Mao Jiang Wu Yu Zhu Jinqiu Sun Yanning Zhang Suraj Patni Aradhye Agarwal Chetan Arora Pihai Sun Kui Jiang Gang Wu Jian Liu Xianming Liu Junjun Jiang Xidan Zhang Jianing Wei Fangjun Wang Zhiming Tan Jiabao Wang Albert Luginov Muhammad Shahzad Seyed Hosseini Aleksander Trajcevski James H. Elder Independent University of Bologna Blue River Technology Oxford Internet Institute University of Surrey ByteDance University of Chinese Academy of Science RGA Inc. Space Research Institute NASU-SSAU Kyiv Ukraine Northwestern Polytechnical University Indian Institute of Technology Delhi Harbin Institute of Technology Fujitsu GuangXi University University of Reading York University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%.

关键词： computer vision Three-dimensional displays conferences Computational modeling Estimation pattern recognition

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Simple In-place Data Augmentation for Surveillance Object Detection

Simple In-place Data Augmentation for Surveillance Object De...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Munkh-Erdene Otgonbold Ganzorig Batnasan Munkhjargal Gochoo Department of Computer Science and Software Engineering United Arab Emirates University UAE Department of Electronics Mongolian University of Science and Technology Mongolia Emirates Center for Mobility Research United Arab Emirates University UAE

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Motivated by the need to improve model performance in traffic monitoring tasks with limited labeled samples, we propose a straightforward augmentation technique tailored for object detection datasets, specifically designed for stationary camera-based applications. Our approach focuses on placing objects in the same positions as the originals to ensure its effectiveness. By applying in-place augmentation on objects from the same camera input image, we address the challenge of overlapping with original and previously selected objects. Through extensive testing on two traffic monitoring datasets, we illustrate the efficacy of our augmentation strategy in improving model performance, particularly in scenarios with limited labeled samples and imbalanced class distributions. Notably, our method achieves comparable performance to models trained on the entire dataset while utilizing only 8.5 percent of the original data. Moreover, we report significant improvements, with mAP@.5 increasing from 0.4798 to 0.5025, and the mAP@.5:.95 rising from 0.29 to 0.3138 on the FishEye8K dataset. These results highlight the potential of our augmentation approach in enhancing object detection models for traffic monitoring applications.

关键词： Image segmentation Surveillance Scalability conferences Object detection Data augmentation Cameras

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Multi-modal Aerial View Image Challenge: Sensor Domain Translation

Multi-modal Aerial View Image Challenge: Sensor Domain Trans...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Spencer Low Oliver Nina Dylan Bowald Angel D. Sappa Nathan Inkawhich Peter Bruns Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH ESPOL Polytechnic University Ecuador Computer Vision Center Spain Air Force Research Laboratory Rome NY University of Utah Salt Lake UT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper describes the design, outcomes, and top methods of the 2nd annual Multi-modal Aerial View Image Challenge (MAVIC) aimed at cross modality aerial image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. Specifically, the challenge centers on translation between synthetic aperture radar (SAR), electro-optical (EO), camera (RGB), and infrared (IR) sensor modalities, a budding area of research that has begun to garner attention. While last year’s inaugural challenge demonstrated the feasibility of SAR→EO translation, this year’s challenge made significant improvements in dataset coverage, sensor variation, experimental design, and methods covering the tasks of SAR→EO, SAR→RGB, SAR→IR, RGB→IR introducing a new dataset called translation. By Multi-modal Aerial Gathered Image Composites (MAGIC); multimodal image translation is available for different comparisons. With a more rigorous set of translation performance metrics, winners were determined from aggregation of L1-norm, LPIPS (Learned Perceptual Image Patch Similarity, and FID (Frechet Inception Distance) scores. The wining methods included the pix2pixHD and LPIPS metrics as loss functions with an aggregated score 5% better separated by the SAR→EO and RGB→IR translation scores.

关键词： Measurement computer vision Design methodology conferences Cameras pattern recognition Synthetic aperture radar

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Multi-Modal Fusion of Event and RGB for Monocular Depth Estimation Using a Unified Transformer-based Architecture

Multi-Modal Fusion of Event and RGB for Monocular Depth Esti...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Anusha Devulapally Md Fahim Faysal Khan Siddharth Advani Vijaykrishnan Narayanan The Pennsylvania State University Samsung Electronics America

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of robotics and autonomous navigation, accurate pixel-level depth estimation has gained significant importance. Event cameras or dynamic vision sensors, capture asynchronous changes in brightness at the pixel level, offering benefits such as high temporal resolution, no motion blur, and a wide dynamic range. However, unlike traditional cameras that measure absolute intensity, event cameras lack the ability to provide scene context. Efficiently combining the advantages of both asynchronous events and synchronous RGB images to enhance depth estimation remains a challenge. In our study, we introduce a unified transformer that combines both event and RGB modalities to achieve precise depth prediction. In contrast to individual transformers for input modalities, a unified transformer model captures inter-modal dependencies and uses self-attention to enhance event-RGB contextual interactions. This approach exceeds the performance of recurrent neural network (RNN) methods used in state-of-the-art models. To encode the temporal information from events, convLSTMs are used before the transformer to improve depth estimation. Our proposed architecture outperforms the existing approaches in terms of absolute mean depth error, achieving state-of-the-art results in most cases. Additionally, the performance is also seen in other metrics like RMSE, absolute relative difference and depth thresholds compared to the existing approaches. The source code is available at: https://***/anusha-devulapally/ER-F2D.

关键词： Measurement computer vision Accuracy Recurrent neural networks Robot vision systems Estimation computer architecture

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Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report

Fast and Accurate Single-Image Depth Estimation on Mobile De...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Raspberry Pi Trading Ltd Cambridge England AI Witchlabs Zurich Switzerland Tencent GY Lab Shenzhen Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan Peoples R China Chinese Acad Sci Inst Automat Nanjing Artificial Intelligence Chip Res Beijing Peoples R China Black Sesame Technol Inc Singapore Singapore Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China Natl Cheng Kung Univ Multimedia & Comp Vis Lab Tainan Taiwan Samsung Res UK Cambridge England OPPO Res Inst Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper.

关键词： Meters computer vision Image resolution conferences Estimation Real-time systems pattern recognition

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Evaluating and Improving Compositional Text-to-Visual Generation

Evaluating and Improving Compositional Text-to-Visual Genera...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Baiqi Li Zhiqiu Lin Deepak Pathak Jiayao Li Yixin Fei Kewen Wu Xide Xia Pengchuan Zhang Graham Neubig Deva Ramanan CMU Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While text-to-visual models now produce photo-realistic images and videos, they struggle with compositional text prompts involving attributes, relationships, and higher-order reasoning such as logic and comparison. In this work, we conduct an extensive human study on GenAI-Bench to evaluate the performance of leading image and video generation models in various aspects of compositional text-to-visual generation. We also compare automated evaluation metrics against our collected human ratings and find that VQAScore – a metric measuring the likelihood that a VQA model views an image as accurately depicting the prompt – significantly outperforms previous metrics such as CLIP-Score. In addition, VQAScore can improve generation in a black-box manner (without finetuning) via simply ranking a few (3 to 9) candidate images. Ranking by VQAScore is 2x to 3x more effective than other scoring methods like PickScore and ImageReward at improving human ratings for DALL-E 3 and Stable Diffusion, especially on compositional prompts that require advanced visio-linguistic reasoning. Lastly, we identify areas for improvement in VQAScore, such as addressing fine-grained visual details. Despite mild limitations, VQAScore serves as the best automated metric as well as reward function for improving prompt alignment. We will release over 80,000 human ratings to facilitate scientific benchmarking of both generative models and automated metrics.

关键词： Measurement Visualization Toxicology Closed box Footwear Cognition pattern recognition

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Event Camera Demosaicing via Swin Transformer and Pixel-focus Loss

Event Camera Demosaicing via Swin Transformer and Pixel-focu...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yunfan Lu Yijie Xu Wenzong Ma Weiyu Guo Hui Xiong AI Thrust Hong Kong University of Science and Technology (Guangzhou)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent research has highlighted improvements in high-quality imaging guided by event cameras, with most of these efforts concentrating on the RGB domain. However, these advancements frequently neglect the unique challenges introduced by the inherent flaws in the sensor design of event cameras in the RAW domain. Specifically, this sensor design results in the partial loss of pixel values, posing new challenges for RAW domain processes like demosaicing. The challenge intensifies as most research in the RAW domain is based on the premise that each pixel contains a value, making the straightforward adaptation of these methods to event camera demosaicing problematic. To end this, we present a Swin-Transformer-based backbone and a pixel-focus loss function for demosaicing with missing pixel values in RAW domain processing. Our core motivation is to refine a general and widely applicable foundational model from the RGB domain for RAW domain processing, thereby broadening the model’s applicability within the entire imaging process. Our method harnesses multi-scale processing and space-to-depth techniques to ensure efficiency and reduce computing complexity. We also proposed the Pixel-focus Loss function for network fine-tuning to improve network convergence based on our discovery of a long-tailed distribution in training loss. Our method has undergone validation on the MIPI Demosaic Challenge dataset, with subsequent analytical experimentation confirming its efficacy. All code and trained models are released here: https://***/yunfanLu/ev-demosaic.

关键词： Training computer vision Computational modeling conferences computer architecture Cameras Transformers

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Exploring the Role of Audio in Video Captioning

Exploring the Role of Audio in Video Captioning

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yuhan Shen Linjie Yang Longyin Wen Haichao Yu Ehsan Elhamifar Heng Wang Northeastern University ByteDance

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M. Though these approaches have achieved significant improvement, the audio modality is often ignored in video captioning. In this work, we present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. Instead of relying on text transcripts extracted via automatic speech recognition (ASR), we argue that learning with raw audio signals can be more beneficial, as audio has additional information including acoustic events, speaker identity, etc. Our contributions are twofold. First, we observed that the model overspecializes to the audio modality when pre-training with both video and audio modality, since the ground truth (i.e., text transcripts) can be solely predicted using audio. We proposed a Modality Balanced Pre-training (MBP) loss to mitigate this issue and significantly improve the performance on downstream tasks. Second, we slice and dice different design choices of the cross-modal module, which may become an information bottleneck and generate inferior results. We proposed new local-global fusion mechanisms to improve information exchange across audio and video. We demonstrate significant improvements by leveraging the audio modality on four datasets, and even outperform the state of the art on some metrics without relying on the text modality as the input.

关键词： Measurement computer vision Computational modeling conferences Predictive models Acoustics Data mining

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