State of the art speech enhancement (SE) models achieve strong performance on neurotypical speech, but their effectiveness is substantially reduced for pathological speech. In this paper, we investigate strategies to address this gap for both predictive and generative SE models, including i) training models from scratch using pathological data, ii) finetuning models pretrained on neurotypical speech with additional data from pathological speakers, and iii) speaker specific personalization using only data from the individual pathological test speaker. Our results show that, despite the limited size of pathological speech datasets, SE models can be successfully trained or finetuned on such data. Finetuning models with data from several pathological speakers yields the largest performance improvements, while speaker specific personalization is less effective, likely due to the small amount of data available per speaker. These findings highlight the challenges and potential strategies for improving SE performance for pathological speakers.
Foundation models (FMs) have transformed computational pathology by providing powerful, general-purpose feature extractors. However, adapting and benchmarking individual FMs for specific diagnostic tasks is often time-consuming and resource-intensive, especially given their scale and diversity. To address this challenge, we introduce Group-Aggregative Selection Multi-Instance Learning (GAS-MIL), a flexible ensemble framework that seamlessly integrates features from multiple FMs, preserving their complementary strengths without requiring manual feature selection or extensive task-specific fine-tuning. Across classification tasks in three cancer datasets-prostate (PANDA), ovarian (UBC-OCEAN), and breast (TCGA-BrCa)-GAS-MIL consistently achieves superior or on-par performance relative to individual FMs and established MIL methods, demonstrating its robustness and generalizability. By enabling efficient integration of heterogeneous FMs, GAS-MIL streamlines model deployment for pathology and provides a scalable foundation for future multimodal and precision oncology applications.
Ekaterina Redekop, Mara Pleasure, Zichen Wang
et al.
The rapid growth of digital pathology and advances in self-supervised deep learning have enabled the development of foundational models for various pathology tasks across diverse diseases. While multimodal approaches integrating diverse data sources have emerged, a critical gap remains in the comprehensive integration of whole-slide images (WSIs) with spatial transcriptomics (ST), which is crucial for capturing critical molecular heterogeneity beyond standard hematoxylin & eosin (H&E) staining. We introduce SPADE, a foundation model that integrates histopathology with ST data to guide image representation learning within a unified framework, in effect creating an ST-informed latent space. SPADE leverages a mixture-of-data experts technique, where experts are created via two-stage imaging feature-space clustering using contrastive learning to learn representations of co-registered WSI patches and gene expression profiles. Pre-trained on the comprehensive HEST-1k dataset, SPADE is evaluated on 20 downstream tasks, demonstrating significantly superior few-shot performance compared to baseline models, highlighting the benefits of integrating morphological and molecular information into one latent space. Code and pretrained weights are available at https://github.com/uclabair/SPADE.
Automated pathology report generation from Whole Slide Images (WSIs) faces two key challenges: (1) lack of semantic content in visual features and (2) inherent information redundancy in WSIs. To address these issues, we propose a novel Historical Report Guided \textbf{Bi}-modal Concurrent Learning Framework for Pathology Report \textbf{Gen}eration (BiGen) emulating pathologists' diagnostic reasoning, consisting of: (1) A knowledge retrieval mechanism to provide rich semantic content, which retrieves WSI-relevant knowledge from pre-built medical knowledge bank by matching high-attention patches and (2) A bi-modal concurrent learning strategy instantiated via a learnable visual token and a learnable textual token to dynamically extract key visual features and retrieved knowledge, where weight-shared layers enable cross-modal alignment between visual features and knowledge features. Our multi-modal decoder integrates both modals for comprehensive diagnostic reports generation. Experiments on the PathText (BRCA) dataset demonstrate our framework's superiority, achieving state-of-the-art performance with 7.4\% relative improvement in NLP metrics and 19.1\% enhancement in classification metrics for Her-2 prediction versus existing methods. Ablation studies validate the necessity of our proposed modules, highlighting our method's ability to provide WSI-relevant rich semantic content and suppress information redundancy in WSIs. Code is publicly available at https://github.com/DeepMed-Lab-ECNU/BiGen.
Shilpi Gupta1, Ekadashi Rajni1*, Afreen Ali1
et al.
Introduction: Methicillin-resistant Staphylococcus aureus (MRSA) has a
high prevalence in hospital settings in India and imposes a serious economic
burden on healthcare resources. Understanding the local prevalence and
evolving antimicrobial resistance patterns of MRSA is crucial for guiding
effective treatment strategies. This study aims to determine the prevalence,
clinico-demographic profile, and antibiotic susceptibility patterns of MRSA
and methicillin-susceptible Staphylococcus aureus (MSSA) isolates.
Methods: This retrospective study analyzed Staphylococcus aureus isolates
collected between June 2021 and May 2023 from blood, pus, sterile body
fluids, respiratory, and urine samples at the Microbiology laboratory of
Mahatma Gandhi Hospital. Isolates were identified as S. aureus and tested
for methicillin resistance using the Vitek 2 Compact system, which employs
an advanced colorimetry method for identification and determines the
minimum inhibitory concentration (MIC) using a broth microdilution
method for antimicrobial susceptibility testing. Results: Of the 481
Staphylococcus aureus isolates analyzed, 264 (55%) were identified as
MRSA. Among the MRSA isolates, the most common source was
pus/wound infections (59%), followed by bloodstream infections (22%).
MRSA isolates showed a susceptibility rate of 56% to gentamicin and 45%
to clindamycin, but only 14% to ciprofloxacin. However, 55% of MSSA
isolates were resistant to ciprofloxacin. All MRSA isolates were susceptible
to daptomycin, teicoplanin, vancomycin, and linezolid. Conclusion: Our
findings underscore the need for continuous MRSA surveillance and
emphasize tailoring local antibiotic guidelines based on resistance patterns.
Targeted antimicrobial stewardship programs and reinforced infection
control protocols, especially for pus/wound infections, are crucial to curb the
spread of resistant strains.
In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. One complication in preparing digital pathology datasets for DNN tasks is variation in tinctorial qualities. A common way to address this is to perform stain normalization on the images. In this study, we show that a well-trained DNN model trained on one batch of histological slides failed to generalize to another batch prepared at a different time from the same tissue blocks, even when stain normalization methods were applied. This study used sample data from a previously reported DNN that was able to identify patients with early stage non-small cell lung cancer (NSCLC) whose tumors did and did not metastasize, with high accuracy, based on training and then testing of digital images from H&E stained primary tumor tissue sections processed at the same time. In this study we obtained a new series of histologic slides from the adjacent recuts of same tissue blocks processed in the same lab but at a different time. We found that the DNN trained on the either batch of slides/images was unable to generalize and failed to predict progression in the other batch of slides/images (AUC_cross-batch = 0.52 - 0.53 compared to AUC_same-batch = 0.74 - 0.81). The failure to generalize did not improve even when the tinctorial difference correction were made through either traditional color-tuning or stain normalization with the help of a Cycle Generative Adversarial Network (CycleGAN) process. This highlights the need to develop an entirely new way to process and collect consistent microscopy images from histologic slides that can be used to both train and allow for the general application of predictive DNN algorithms.
The spatial composition and cellular heterogeneity of the tumor microenvironment plays a critical role in cancer development and progression. High-definition pathology imaging of tumor biopsies provide a high-resolution view of the spatial organization of different types of cells. This allows for systematic assessment of intra- and inter-patient spatial cellular interactions and heterogeneity by integrating accompanying patient-level genomics data. However, joint modeling across tumor biopsies presents unique challenges due to non-conformability (lack of a common spatial domain across biopsies) as well as high-dimensionality. To address this problem, we propose the Dual random effect and main effect selection model for Spatially structured regression model (DreameSpase). DreameSpase employs a Bayesian variable selection framework that facilitates the assessment of spatial heterogeneity with respect to covariates both within (through fixed effects) and between spaces (through spatial random effects) for non-conformable spatial domains. We demonstrate the efficacy of DreameSpase via simulations and integrative analyses of pathology imaging and gene expression data obtained from $335$ melanoma biopsies. Our findings confirm several existing relationships, e.g. neutrophil genes being associated with both inter- and intra-patient spatial heterogeneity, as well as discovering novel associations. We also provide freely available and computationally efficient software for implementing DreameSpase.
Emely Rosbach, Jonas Ammeling, Sebastian Krügel
et al.
Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, such as confirmation bias caused by false confirmation when erroneous human opinions are reinforced by inaccurate AI output. This bias may worsen when time pressure, ubiquitously present in routine pathology, strains practitioners' cognitive resources. We quantified confirmation bias triggered by AI-induced false confirmation and examined the role of time constraints in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results suggest that AI integration may fuel confirmation bias, evidenced by a statistically significant positive linear-mixed-effects model coefficient linking AI recommendations mirroring flawed human judgment and alignment with system advice. Conversely, time pressure appeared to weaken this relationship. These findings highlight potential risks of AI use in healthcare and aim to support the safe integration of clinical decision support systems.
Automatic pathological speech detection approaches yield promising results in identifying various pathologies. These approaches are typically designed and evaluated for phonetically-controlled speech scenarios, where speakers are prompted to articulate identical phonetic content. While gathering controlled speech recordings can be laborious, spontaneous speech can be conveniently acquired as potential patients navigate their daily routines. Further, spontaneous speech can be valuable in detecting subtle and abstract cues of pathological speech. Nonetheless, the efficacy of automatic pathological speech detection for spontaneous speech remains unexplored. This paper analyzes the influence of speech mode on pathological speech detection approaches, examining two distinct categories of approaches, i.e., classical machine learning and deep learning. Results indicate that classical approaches may struggle to capture pathology-discriminant cues in spontaneous speech. In contrast, deep learning approaches demonstrate superior performance, managing to extract additional cues that were previously inaccessible in non-spontaneous speech
Tim Lenz, Omar S. M. El Nahhas, Marta Ligero
et al.
Deep Learning models have been successfully utilized to extract clinically actionable insights from routinely available histology data. Generally, these models require annotations performed by clinicians, which are scarce and costly to generate. The emergence of self-supervised learning (SSL) methods remove this barrier, allowing for large-scale analyses on non-annotated data. However, recent SSL approaches apply increasingly expansive model architectures and larger datasets, causing the rapid escalation of data volumes, hardware prerequisites, and overall expenses, limiting access to these resources to few institutions. Therefore, we investigated the complexity of contrastive SSL in computational pathology in relation to classification performance with the utilization of consumer-grade hardware. Specifically, we analyzed the effects of adaptations in data volume, architecture, and algorithms on downstream classification tasks, emphasizing their impact on computational resources. We trained breast cancer foundation models on a large public patient cohort and validated them on various downstream classification tasks in a weakly supervised manner on two external public patient cohorts. Our experiments demonstrate that we can improve downstream classification performance whilst reducing SSL training duration by 90%. In summary, we propose a set of adaptations which enable the utilization of SSL in computational pathology in non-resource abundant environments.
Large Vision-Language Models (LVLMs) have achieved significant success in recent years, and they have been extended to the medical domain. Although demonstrating satisfactory performance on medical Visual Question Answering (VQA) tasks, Medical LVLMs (MLVLMs) suffer from the hallucination problem, which makes them fail to diagnose complex pathologies. Moreover, they readily fail to learn minority pathologies due to imbalanced training data. We propose two prompting strategies for MLVLMs that reduce hallucination and improve VQA performance. In the first strategy, we provide a detailed explanation of the queried pathology. In the second strategy, we fine-tune a cheap, weak learner to achieve high performance on a specific metric, and textually provide its judgment to the MLVLM. Tested on the MIMIC-CXR-JPG and Chexpert datasets, our methods significantly improve the diagnostic F1 score, with the highest increase being 0.27. We also demonstrate that our prompting strategies can be extended to general LVLM domains. Based on POPE metrics, it effectively suppresses the false negative predictions of existing LVLMs and improves Recall by approximately 0.07.