Search Results for author: Andre Esteva

Found 6 papers, 0 papers with code

Improved Multimodal Fusion for Small Datasets with Auxiliary Supervision

no code implementations1 Apr 2023 Gregory Holste, Douwe van der Wal, Hans Pinckaers, Rikiya Yamashita, Akinori Mitani, Andre Esteva

We validate the proposed approaches on prostate cancer diagnosis from paired histopathology imaging and tabular clinical features.

MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation

no code implementations29 Sep 2021 Zheng Yuan, Andre Esteva, ran Xu

We also curate a histopathology meta dataset - a benchmark dataset for training and validating models on out-of-distribution performance across a range of cancer types.

Domain Generalization Few-Shot Learning +3

Data Shapley Valuation for Efficient Batch Active Learning

no code implementations16 Apr 2021 Amirata Ghorbani, James Zou, Andre Esteva

In this work, we introduce Active Data Shapley (ADS) -- a filtering layer for batch active learning that significantly increases the efficiency of active learning by pre-selecting, using a linear time computation, the highest-value points from an unlabeled dataset.

Active Learning

CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization

no code implementations17 Jun 2020 Andre Esteva, Anuprit Kale, Romain Paulus, Kazuma Hashimoto, Wenpeng Yin, Dragomir Radev, Richard Socher

The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines.

Abstractive Text Summarization Information Retrieval +3

Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning

no code implementations4 Dec 2016 Yunzhu Li, Andre Esteva, Brett Kuprel, Rob Novoa, Justin Ko, Sebastian Thrun

Dense object detection and temporal tracking are needed across applications domains ranging from people-tracking to analysis of satellite imagery over time.

Dense Object Detection object-detection

Affordances Provide a Fundamental Categorization Principle for Visual Scenes

no code implementations19 Nov 2014 Michelle R. Greene, Christopher Baldassano, Andre Esteva, Diane M. Beck, Li Fei-Fei

Traditional models of visual perception posit that scene categorization is achieved through the recognition of a scene's objects, yet these models cannot account for the mounting evidence that human observers are relatively insensitive to the local details in an image.

Cannot find the paper you are looking for? You can Submit a new open access paper.