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Found 18 papers, 2 papers with code
Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling
Artificial Intelligence (AI) has great potential to improve health outcomes by training systems on vast digitized clinical datasets.
Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images
Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream tasks.
Virchow: A Million-Slide Digital Pathology Foundation Model
The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer.
Deep conditional transformation models for survival analysis
An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of electronic health records.
Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction
Necrosis is routinely assessed post-chemotherapy from histology slides on resection specimens where necrosis ratio is defined as the ratio of necrotic tumor to overall tumor.
Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation
Instead of annotating all pixels from cancer and non-cancer regions on giga-pixel whole slide images, an iterative process of annotating mislabeled regions from a segmentation model and training/finetuning the model with the additional annotation can reduce the time.
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting
EPIC-Survival bridges encoding and aggregation into an end-to-end survival modelling approach, while introducing stratification boosting to encourage the model to not only optimize ranking, but also to discriminate between risk groups.
Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment
Osteosarcoma is the most common malignant primary bone tumor.
Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning
A loss with respect to the slide label is backpropagated through an integrated CNN model to $k$ input tiles that are used to represent each part.
Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation
Pathologic analysis of surgical excision specimens for breast carcinoma is important to evaluate the completeness of surgical excision and has implications for future treatment.
Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual Dictionary
Combinations of these clusters were significant in multivariate analysis.
Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology
In the field of computational pathology, the use of decision support systems powered by state-of-the-art deep learning solutions has been hampered by the lack of large labeled datasets.
DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem
To overcome this problem we present a novel PET image reconstruction technique based on a deep convolutional encoder-decoder network, that takes PET sinogram data as input and directly outputs full PET images.
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations
Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions.
Computational Pathology: Challenges and Promises for Tissue Analysis
The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer.
Boosting Convolutional Features for Robust Object Proposals
Modern detection algorithms like Regions with CNNs (Girshick et al., 2014) rely on Selective Search (Uijlings et al., 2013) to propose regions which with high probability represent objects, where in turn CNNs are deployed for classification.
Early Recognition of Human Activities from First-Person Videos Using Onset Representations
In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint.
Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets
The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible.
