Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification

Computer-aided pathology diagnosis based on the classification of Whole Slide Image (WSI) plays an important role in clinical practice, and it is often formulated as a weakly-supervised Multiple Instance Learning (MIL) problem. Existing methods solve this problem from either a bag classification or an instance classification perspective. In this paper, we propose an end-to-end weakly supervised knowledge distillation framework (WENO) for WSI classification, which integrates a bag classifier and an instance classifier in a knowledge distillation framework to mutually improve the performance of both classifiers. Specifically, an attention-based bag classifier is used as the teacher network, which is trained with weak bag labels, and an instance classifier is used as the student network, which is trained using the normalized attention scores obtained from the teacher network as soft pseudo labels for the instances in positive bags. An instance feature extractor is shared between the teacher and the student to further enhance the knowledge exchange between them. In addition, we propose a hard positive instance mining strategy based on the output of the student network to force the teacher network to keep mining hard positive instances. WENO is a plug-and-play framework that can be easily applied to any existing attention-based bag classification methods. Extensive experiments on five datasets demonstrate the efficiency of WENO. Code is available at https://github.com/miccaiif/WENO.Comment: Accepted by NeurIPS 202

Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)

3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. The code is available at: \href{https://github.com/fukexue/RGM}{https://github.com/fukexue/RGM}.Comment: accepted by TPAMI 2022. arXiv admin note: substantial text overlap with arXiv:2103.0425

The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification

This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4. Since a WSI is too large and needs to be divided into patches for processing, WSI classification is commonly approached as a Multiple Instance Learning (MIL) problem. In this context, each WSI is considered a bag, and the obtained patches are treated as instances. The objective of FSWC is to classify both bags and instances with only a limited number of labeled bags. Unlike conventional few-shot learning problems, FSWC poses additional challenges due to its weak bag labels within the MIL framework. Drawing inspiration from the recent achievements of vision-language models (V-L models) in downstream few-shot classification tasks, we propose a two-level prompt learning MIL framework tailored for pathology, incorporating language prior knowledge. Specifically, we leverage CLIP to extract instance features for each patch, and introduce a prompt-guided pooling strategy to aggregate these instance features into a bag feature. Subsequently, we employ a small number of labeled bags to facilitate few-shot prompt learning based on the bag features. Our approach incorporates the utilization of GPT-4 in a question-and-answer mode to obtain language prior knowledge at both the instance and bag levels, which are then integrated into the instance and bag level language prompts. Additionally, a learnable component of the language prompts is trained using the available few-shot labeled data. We conduct extensive experiments on three real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer, demonstrating the notable performance of the proposed method in bag and instance classification. All codes will be made publicly accessible

Delay-dependent robust H∞ filtering for linear stochastic systems with uncertainties

This paper investigates the robustH∞ filtering problem for continuous –time linear stochastic systems with time-vary state delay and parameter uncertainty. The aim is to design a linear filter such that the filtering error dynamics system is exponentially stable in mean square and assuring a prescribedH∞ performance level .Applying the descriptor model transformation, construct a new Lyapunov-Krasovskii functional . By introducing some free weighting matrices, avoiding any product term of Lyapunov matrices and system matrices，so it is not necessary for system matrices to do any constraint in the process of the design of filters, to a great extent , which make the design of filters have less conservative. For system without uncertainty and with uncertainty case, to guarantee the existence of desired robust H∞ filters, sufficient conditions are proposed respectively in terms of linear matrix inequalities (LMIs), The results obtained are less conservative than existing ones. Numerical examples demonstrate the proposed approaches are effective and are an improvement over previous ones

Multi-population genome-wide association study implicates immune and non-immune factors in pediatric steroid-sensitive nephrotic syndrome

Genetics; Minimal change disease; Paediatric kidney diseaseGenética; Enfermedad de cambios mínimos; Enfermedad renal pediátricaGenètica; Malaltia de canvis mínims; Malaltia renal pediàtricaPediatric steroid-sensitive nephrotic syndrome (pSSNS) is the most common childhood glomerular disease. Previous genome-wide association studies (GWAS) identified a risk locus in the HLA Class II region and three additional independent risk loci. But the genetic architecture of pSSNS, and its genetically driven pathobiology, is largely unknown. Here, we conduct a multi-population GWAS meta-analysis in 38,463 participants (2440 cases). We then conduct conditional analyses and population specific GWAS. We discover twelve significant associations-eight from the multi-population meta-analysis (four novel), two from the multi-population conditional analysis (one novel), and two additional novel loci from the European meta-analysis. Fine-mapping implicates specific amino acid haplotypes in HLA-DQA1 and HLA-DQB1 driving the HLA Class II risk locus. Non-HLA loci colocalize with eQTLs of monocytes and numerous T-cell subsets in independent datasets. Colocalization with kidney eQTLs is lacking but overlap with kidney cell open chromatin suggests an uncharacterized disease mechanism in kidney cells. A polygenic risk score (PRS) associates with earlier disease onset. Altogether, these discoveries expand our knowledge of pSSNS genetic architecture across populations and provide cell-specific insights into its molecular drivers. Evaluating these associations in additional cohorts will refine our understanding of population specificity, heterogeneity, and clinical and molecular associations.K.I., K.N., and K.T. were supported by the Japan Agency for Medical Research and Development (AMED) under grant number JP17km0405108h0005. K.T. was supported by the Japan Agency for Medical Research and Development (AMED) under grants JP17km0405205h0002 and 18km0405205h0003. K.I., T.H., C.N., and K.N. were supported by the Japan Society for the Promotion of Science (JSPS) under Grant-in-Aid for Scientific Research fostering Joint International Research (B) 18KK0244. K.I., X.J., T.H., C.N., and K.N. were supported by the Japan Society for the Promotion of Science (JSPS) under Grant-in-Aid for Scientific Research fostering Joint International Research (B) 21KK0147. This work is supported by the Department of Defense (PR190746, PR212415) to S.S-C., by the National Center for Advancing Translational Sciences, National Institutes of Health (Grant Number UL1TR001873) to S.S-C., and by the National Institute of Health Grant RC2DK122397, M.Sam, S.S-C., M.R.P., and F.H. A.M. received support from the American Society of Nephrology KidneyCure Ben J. Lipps Research Fellowship. Y.G. received support from the NEPTUNE Career Development Award. P.R. and H.D. were funded by European Research Council grant ERC-2012- ADG_20120314 (grant agreement 322947) and Agence Nationale pour la Recherche “Genetransnephrose” grant ANR-16-CE17-004-01. M.Sam. was supported by NIH grants R01DK119380, 2U54DK083912, and a gift from The Pura Vida Kidney Foundation. The Nephrotic Syndrome Study Network (NEPTUNE) is part of the Rare Diseases Clinical Research Network (RDCRN), which is funded by the National Institutes of Health (NIH) and led by the National Center for Advancing Translational Sciences (NCATS) through its Division of Rare Diseases Research Innovation (DRDRI). NEPTUNE is funded under grant number U54DK083912 as a collaboration between NCATS and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Additional funding and/or programmatic support is provided by the University of Michigan, NephCure Kidney International, and the Halpin Foundation. RDCRN consortia are supported by the RDCRN Data Management and Coordinating Center (DMCC), funded by NCATS and the National Institute of Neurological Disorders and Stroke (NINDS) under U2CTR002818. The authors wish to thank Seong Kyu Han, Ph.D. (Boston Children’s Hospital and Harvard Medical School) for his assistance in creating figures