Search Results for author: Saeed Hassanpour

Found 22 papers, 5 papers with code

A Petri Dish for Histopathology Image Analysis

no code implementations29 Jan 2021 Jerry Wei, Arief Suriawinata, Bing Ren, Xiaoying Liu, Mikhail Lisovsky, Louis Vaickus, Charles Brown, Michael Baker, Naofumi Tomita, Lorenzo Torresani, Jason Wei, Saeed Hassanpour

With the rise of deep learning, there has been increased interest in using neural networks for histopathology image analysis, a field that investigates the properties of biopsy or resected specimens traditionally manually examined under a microscope by pathologists.

Transfer Learning

Resolution-Based Distillation for Efficient Histology Image Classification

no code implementations11 Jan 2021 Joseph DiPalma, Arief A. Suriawinata, Laura J. Tafe, Lorenzo Torresani, Saeed Hassanpour

Our results show that a combination of KD and self-supervision allows the student model to approach, and in some cases, surpass the classification accuracy of the teacher, while being much more efficient.

General Classification Knowledge Distillation

Development and Evaluation of a Deep Neural Network for Histologic Classification of Renal Cell Carcinoma on Biopsy and Surgical Resection Slides

no code implementations30 Oct 2020 Mengdan Zhu, Bing Ren, Ryland Richards, Matthew Suriawinata, Naofumi Tomita, Saeed Hassanpour

In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal.

General Classification

Sensitivity and Specificity Evaluation of Deep Learning Models for Detection of Pneumoperitoneum on Chest Radiographs

no code implementations17 Oct 2020 Manu Goyal, Judith Austin-Strohbehn, Sean J. Sun, Karen Rodriguez, Jessica M. Sin, Yvonne Y. Cheung, Saeed Hassanpour

State-of-the-art deep learning models (ResNet101, InceptionV3, DenseNet161, and ResNeXt101) were trained on a subset of this dataset, and the automated classification performance was evaluated on the rest of the dataset by measuring the AUC, sensitivity, and specificity for each model.

Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification

no code implementations29 Sep 2020 Jerry Wei, Arief Suriawinata, Bing Ren, Xiaoying Liu, Mikhail Lisovsky, Louis Vaickus, Charles Brown, Michael Baker, Mustafa Nasir-Moin, Naofumi Tomita, Lorenzo Torresani, Jason Wei, Saeed Hassanpour

Based on the nature of histopathology images, a range of difficulty inherently exists among examples, and, since medical datasets are often labeled by multiple annotators, annotator agreement can be used as a natural proxy for the difficulty of a given example.

Curriculum Learning General Classification +1

A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection

1 code implementation15 Jul 2020 Manu Goyal, Saeed Hassanpour

Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication of diabetes.

Diabetic Foot Ulcer Detection

Difficulty Translation in Histopathology Images

1 code implementation27 Apr 2020 Jerry Wei, Arief Suriawinata, Xiaoying Liu, Bing Ren, Mustafa Nasir-Moin, Naofumi Tomita, Jason Wei, Saeed Hassanpour

Our model comprises a scorer, which provides an output confidence to measure the difficulty of images, and an image translator, which learns to translate images from easy-to-classify to hard-to-classify using a training set defined by the scorer.


Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Ontology and Corpus-based Semantic Representation for Biomedical Concepts

no code implementations14 Apr 2020 Steven Jiang, Weiyi Wu, Naofumi Tomita, Craig Ganoe, Saeed Hassanpour

For the ontology-based part, we use the Medical Subject Headings (MeSH) ontology and three state-of-the-art ontology-based similarity measures.

Word Embeddings

Self-Supervised Contextual Language Representation of Radiology Reports to Improve the Identification of Communication Urgency

no code implementations5 Dec 2019 Xing Meng, Craig H. Ganoe, Ryan T. Sieberg, Yvonne Y. Cheung, Saeed Hassanpour

We pre-trained the BERT model on a large unlabeled corpus of radiology reports and used the resulting contextual representations in a final text classifier for communication urgency.

Self-Supervised Learning

Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities

no code implementations26 Nov 2019 Manu Goyal, Thomas Knackstedt, Shaofeng Yan, Saeed Hassanpour

Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer.

General Classification Image Classification

Automatic Post-Stroke Lesion Segmentation on MR Images using 3D Residual Convolutional Neural Network

no code implementations25 Nov 2019 Naofumi Tomita, Steven Jiang, Matthew E. Maeder, Saeed Hassanpour

In this paper, we demonstrate the feasibility and performance of deep residual neural networks for volumetric segmentation of irreversibly damaged brain tissue lesions on T1-weighted MRI scans for chronic stroke patients.

Lesion Segmentation

Predicting colorectal polyp recurrence using time-to-event analysis of medical records

no code implementations18 Nov 2019 Lia X. Harrington, Jason W. Wei, Arief A. Suriawinata, Todd A. MacKenzie, Saeed Hassanpour

Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence.

Generative Image Translation for Data Augmentation in Colorectal Histopathology Images

1 code implementation13 Oct 2019 Jerry Wei, Arief Suriawinata, Louis Vaickus, Bing Ren, Xiaoying Liu, Jason Wei, Saeed Hassanpour

We present an image translation approach to generate augmented data for mitigating data imbalances in a dataset of histopathology images of colorectal polyps, adenomatous tumors that can lead to colorectal cancer if left untreated.

Data Augmentation Image Classification +1

Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

1 code implementation31 Jan 2019 Jason W. Wei, Laura J. Tafe, Yevgeniy A. Linnik, Louis J. Vaickus, Naofumi Tomita, Saeed Hassanpour

It achieved a kappa score of 0. 525 and an agreement of 66. 6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0. 485 and agreement of 62. 7% on this test set.

General Classification Image Classification +2

Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides

1 code implementation20 Nov 2018 Naofumi Tomita, Behnaz Abdollahi, Jason Wei, Bing Ren, Arief Suriawinata, Saeed Hassanpour

Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results.

Crop Classification General Classification +2

Region of Interest Detection in Dermoscopic Images for Natural Data-augmentation

no code implementations27 Jul 2018 Manu Goyal, Saeed Hassanpour, Moi Hoon Yap

The localization methods proved superior to the segmentation method in ROI detection on skin lesion datasets.

Data Augmentation Object Localization

Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks

no code implementations28 Nov 2017 Manu Goyal, Moi Hoon Yap, Saeed Hassanpour

Melanoma is clinically difficult to distinguish from common benign skin lesions, particularly melanocytic naevus and seborrhoeic keratosis.

Semantic Segmentation Transfer Learning

Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images

no code implementations5 Mar 2017 Bruno Korbar, Andrea M. Olofson, Allen P. Miraflor, Katherine M. Nicka, Matthew A. Suriawinata, Lorenzo Torresani, Arief A. Suriawinata, Saeed Hassanpour

In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps.

General Classification whole slide images

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