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Learning to Exploit Temporal Structure for Biomedical vision-Language Processing

Learning to Exploit Temporal Structure for Biomedical Vision...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bannur, Shruthi Hyland, Stephanie Liu, Qianchu Perez-Garcia, Fernando Ilse, Maximilian Castro, Daniel C. Boecking, Benedikt Sharma, Harshita Bouzid, Kenza Thieme, Anja Schwaighofer, Anton Wetscherek, Maria Lungren, Matthew P. Nori, Aditya Alvarez-Valle, Javier Oktay, Ozan Microsoft Hlth Futures Redmond WA 98052 USA

ISBN: (纸本)9798350301298

Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data.

关键词： cell microscopy Medical and biological vision

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Optimization-Inspired Cross-Attention Transformer for Compressive Sensing

Optimization-Inspired Cross-Attention Transformer for Compre...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Song, Jiechong Mou, Chong Wang, Shiqi Ma, Siwei Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

By integrating certain optimization solvers with deep neural networks, deep unfolding network (DUN) with good interpretability and high performance has attracted growing attention in compressive sensing (CS). However, existing DUNs often improve the visual quality at the price of a large number of parameters and have the problem of feature information loss during iteration. In this paper, we propose an Optimization-inspired Cross-attention Transformer (OCT) module as an iterative process, leading to a lightweight OCT-based Unfolding Framework (OCTUF) for image CS. Specifically, we design a novel Dual Cross Attention (Dual-CA) sub-module, which consists of an Inertia-Supplied Cross Attention (ISCA) block and a Projection-Guided Cross Attention (PGCA) block. ISCA block introduces multi-channel inertia forces and increases the memory effect by a cross attention mechanism between adjacent iterations. And, PGCA block achieves an enhanced information interaction, which introduces the inertia force into the gradient descent step through a cross attention block. Extensive CS experiments manifest that our OCTUF achieves superior performance compared to state-of-the-art methods while training lower complexity. Codes are available at https://***/songjiechong/OCTUF.

关键词： Low-level vision

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The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector

The Treasure Beneath Multiple Annotations: An Uncertainty-aw...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhou, Caixia Huang, Yaping Pu, Mengyang Guan, Qingji Huang, Li Ling, Haibin Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China SUNY Stony Brook Dept Comp Sci Stony Brook NY USA

ISBN: (纸本)9798350301298

Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voting process, ignoring the inherent ambiguity of edges and labeling bias of annotators. In this paper, we propose a novel uncertainty-aware edge detector (UAED), which employs uncertainty to investigate the subjectivity and ambiguity of diverse annotations. Specifically, we first convert the deterministic label space into a learnable Gaussian distribution, whose variance measures the degree of ambiguity among different annotations. Then we regard the learned variance as the estimated uncertainty of the predicted edge maps, and pixels with higher uncertainty are likely to be hard samples for edge detection. Therefore we design an adaptive weighting loss to emphasize the learning from those pixels with high uncertainty, which helps the network to gradually concentrate on the important pixels. UAED can be combined with various encoder-decoder backbones, and the extensive experiments demonstrate that UAED achieves superior performance consistently across multiple edge detection benchmarks. The source code is available at https://***/ZhouCX117/UAED.

关键词： Low-level vision

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Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

Mask DINO: Towards A Unified Transformer-based Framework for...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Feng Zhang, Hao Xu, Huaizhe Liu, Shilong Zhang, Lei Ni, Lionel M. Shum, Heimg-Yeung Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tsinghua Univ Inst AI Dept CST BNIZist Ctr Beijing Peoples R China Int Digital Econ Acad IDEA Shenzhen Peoples R China Hong Kong Univ Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9798350301298

In this paper we present Mask DINO, a unified object detection and segmentation framework. Mask DINO extends DINO (DETR with Improved Denoising Anchor Boxes) by adding a mask prediction branch which supports all image segmentation tasks (instance, panoptic, and semantic). It makes use of the query embeddings from DINO to dot-product a high-resolution pixel embedding map to predict a set of binary masks. Some key components in DINO are extended for segmentation through a shared architecture and training process. Mask DINO is simple, efficient, and scalable, and it can benefit from joint large-scale detection and segmentation datasets. Our experiments show that Mask DINO significantly outperforms all existing specialized segmentation methods, both on a ResNet-50 backbone and a pre-trained model with SwinL backbone. Notably, Mask DINO establishes the best results to date on instance segmentation (54.5 AP on COCO), panoptic segmentation (59.4 PQ on COCO), and semantic segmentation (60.8 mIoU on ADE20K) among models under one billion parameters. Code is available at https://github. com/ IDEAResearch/MaskDINO.

关键词： vision applications and systems

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Label-Free Liver Tumor Segmentation

Label-Free Liver Tumor Segmentation

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hu, Qixin Chen, Yixiong Xiao, Junfei Sun, Shuwen Chen, Jieneng Yuille, Alan Zhou, Zongwei Huazhong Univ Sci & Technol Wuhan Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Peoples R China Johns Hopkins Univ Baltimore MD USA Nanjing Med Univ Affiliated Hosp 1 Nanjing Peoples R China

ISBN: (纸本)9798350301298

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors;(II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors-this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.

关键词： cell microscopy Medical and biological vision

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SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

SegFormer3D: an Efficient Transformer for 3D Medical Image S...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Perera, Shehan Navard, Pouyan Yilmaz, Alper Ohio State Univ Photogrammetr Comp Vis Lab Columbus OH 43210 USA

ISBN: (纸本)9798350365474

The adoption of vision Transformers (ViTs) based architectures represents a significant advancement in 3D Medical Image (MI) segmentation, surpassing traditional Convolutional Neural Network (CNN) models by enhancing global contextual understanding. While this paradigm shift has significantly enhanced 3D segmentation performance, state-of-the-art architectures require extremely large and complex architectures with large scale computing resources for training and deployment. Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles in both model generalization and convergence. In response to these challenges and to demonstrate that lightweight models are a valuable area of research in 3D medical imaging, we present SegFormer3D, a hierarchical Transformer that calculates attention across multiscale volumetric features. Additionally, SegFormer3D avoids complex decoders and uses an all-MLP decoder to aggregate local and global attention features to produce highly accurate segmentation masks. The proposed memory efficient Transformer preserves the performance characteristics of a significantly larger model in a compact design. SegFormer3D democratizes deep learning for 3D medical image segmentation by offering a model with 33x less parameters and a 13x reduction in GFLOPS compared to the current state-of-the-art (SOTA). We benchmark SegFormer3D against the current SOTA models on three widely used datasets Synapse, BRaTs, and ACDC, achieving competitive results. Code: https://***/OSUPCVLab/***

关键词： 3D Medical Image Segmentation ACDC Attention BraTs Deep Learning Efficient Attention Segmentation Synapse Transformers vision Transformers

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CRAFT: Concept Recursive Activation FacTorization for Explainability

CRAFT: Concept Recursive Activation FacTorization for Explai...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Fel, Thomas Picard, Agustin Bethune, Louis Boissin, Thibaut Vigouroux, David Colin, Julien Cadene, Remi Serre, Thomas Brown Univ Carney Inst Brain Sci Providence RI 02912 USA Sorbonne Univ CNRS Paris France Univ Toulouse Artificial & Nat Intelligence Toulouse Inst Toulouse France Inst Rech Technol St Exupery Toulouse France SNCF Innovat & Res Div St Denis France Scalian Rennes France

ISBN: (纸本)9798350301298

Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity as a type of explainability method. However, recent research has exposed the limited practical value of these methods, attributed in part to their narrow focus on the most prominent regions of an image - revealing "where" the model looks, but failing to elucidate "what" the model sees in those areas. In this work, we try to fill in this gap with CRAFT - a novel approach to identify both "what" and "where" by generating concept-based explanations. We introduce 3 new ingredients to the automatic concept extraction literature: (i) a recursive strategy to detect and decompose concepts across layers, (ii) a novel method for a more faithful estimation of concept importance using Sobol indices, and (iii) the use of implicit differentiation to unlock Concept Attribution Maps. We conduct both human and computer vision experiments to demonstrate the benefits of the proposed approach. We show that the proposed concept importance estimation technique is more faithful to the model than previous methods. When evaluating the usefulness of the method for human experimenters on a human-centered utility benchmark, we find that our approach significantly improves on two of the three test scenarios.

关键词： Explainable computer vision

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Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graph

Multi-Granularity Archaeological Dating of Chinese Bronze Di...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhou, Rixin Wei, Jiafu Zhang, Qian Qi, Ruihua Yang, Xi Li, Chuntao Jilin Univ Sch Artificial Intelligence Jilin Peoples R China MoE Engn Res Ctr Knowledge Driven Human Machine Intel Beijing Peoples R China Jilin Univ Sch Archaeol Jilin Peoples R China

ISBN: (纸本)9798350301298

The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history. Current archaeology depends on trained experts to carry out bronze dating, which is time-consuming and labor-intensive. For such dating, in this study, we propose a learning-based approach to integrate advanced deep learning techniques and archaeological knowledge. To achieve this, we first collect a large-scale image dataset of bronze dings, which contains richer attribute information than other existing fine-grained datasets. Second, we introduce a multihead classifier and a knowledge-guided relation graph to mine the relationship between attributes and the ding era. Third, we conduct comparison experiments with various existing methods, the results of which show that our dating method achieves a state-of-the-art performance. We hope that our data and applied networks will enrich fine-grained classification research relevant to other interdisciplinary areas of expertise. The dataset and source code used are included in our supplementary materials, and will be open after submission owing to the anonymity policy. Source codes and data are available at: https://***/zhourixin/bronze-Ding

关键词： detection recognition: Categorization retrieval

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Q: How to Specialize Large vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images

Q: How to Specialize Large Vision-Language Models to Data-Sc...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Khan, Zaid Kumar, Vijay B. G. Schulter, Samuel Yu, Xiang Fu, Yun Chandraker, Manmohan Northeastern Univ Boston MA 02115 USA NEC Labs Amer Irving TX USA Amazon Seattle WA USA Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9798350301298

Finetuning a large vision language model (VLM) on a target dataset after large scale pretraining is a dominant paradigm in visual question answering (VQA). Datasets for specialized tasks such as knowledge-based VQA or VQA in non natural-image domains are orders of magnitude smaller than those for general-purpose VQA. While collecting additional labels for specialized tasks or domains can be challenging, unlabeled images are often available. We introduce SelTDA (Self-Taught Data Augmentation), a strategy for finetuning large VLMs on small-scale VQA datasets. SelTDA uses the VLM and target dataset to build a teacher model that can generate question-answer pseudolabels directly conditioned on an image alone, allowing us to pseudolabel unlabeled images. SelTDA then finetunes the initial VLM on the original dataset augmented with freshly pseudolabeled images. We describe a series of experiments showing that our self-taught data augmentation increases robustness to adversarially searched questions, counterfactual examples and rephrasings, improves domain generalization, and results in greater retention of numerical reasoning skills. The proposed strategy requires no additional annotations or architectural modifications, and is compatible with any modern encoder-decoder multimodal transformer. Code available at https://***/codezakh/SelTDA.

关键词： and reasoning language vision

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Co-designing a Sub-millisecond Latency Event-based Eye Tracking System with Submanifold Sparse CNN

Co-designing a Sub-millisecond Latency Event-based Eye Track...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Baoheng Gao, Yizhao Li, Jingyuan So, Hayden Kwok-Hay Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350365474

Eye-tracking technology is integral to numerous consumer electronics applications, particularly in the realm of virtual and augmented reality (VR/AR). These applications demand solutions that excel in three crucial aspects: low-latency, low-power consumption, and precision. Yet, achieving optimal performance across all these fronts presents a formidable challenge, necessitating a balance between sophisticated algorithms and efficient backend hardware implementations. In this study, we tackle this challenge through a synergistic software/hardware co-design of the system with an event camera. Leveraging the inherent sparsity of event-based input data, we integrate a novel sparse FPGA dataflow accelerator customized for submanifold sparse convolution neural networks (SCNN). The SCNN implemented on the accelerator can efficiently extract the embedding feature vector from each representation of event slices by only processing the non-zero activations. Subsequently, these vectors undergo further processing by a gated recurrent unit (GRU) and a fully connected layer on the host CPU to generate the eye centers. Deployment and evaluation of our system reveal outstanding performance metrics. On the Event-based Eye-Tracking-AIS2024 dataset, our system achieves 81% p5 accuracy, 99.5% p10 accuracy, and 3.71 Mean Euclidean Distance with 0.7 ms latency while only consuming 2.29 mJ per inference. Notably, our solution opens up opportunities for future eye-tracking systems. Code is available at https://***/CASRHKU/ESDA/tree/eye_tracking.

关键词： dynamic vision sensor event camera event-based vision eye tracking FPGA hardware-software codesign sparse processing

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